Jobs Data Engineer

We are looking for AVEVA PI System Engineers at Middle and Senior levels to join our team and work on industrial data solutions for international customers.

You’ll work with the AVEVA PI System ecosystem, helping customers collect, structure, contextualize, analyze, and get value from operational data.

Depending on your experience, you can contribute as a PI System Engineer or take ownership of technical delivery as a Senior Engineer.
 

What you’ll do

• Implement, configure, maintain, and support AVEVA PI System solutions.
• Work with industrial operational data and help structure it into meaningful, reliable information.
• Design and develop Asset Framework (AF) structures and information models.
• Work with Event Frames, PI Analytics, calculations, and data contextualization.
• Troubleshoot technical issues and support PI System environments in production.
• Contribute to PI System implementations, upgrades, migrations, and optimization projects.
• Create technical documentation, reusable templates, standards, and best practices.
• Collaborate with engineers, consultants, and customers to deliver reliable and scalable solutions.
   

For PI System Engineer

We’d expect you to have:

• 2+ years of hands-on experience with AVEVA PI System in application support, implementation, or technical project delivery.
• Good understanding of PI System concepts, including Asset Framework, information modelling, calculations, data contextualization, and operational data integration.
• Experience troubleshooting and supporting PI System environments.
• Ability to work independently while knowing when to involve more experienced colleagues.
• Interest in developing towards a customer-facing PI System consulting role.
   

For Senior PI System Engineer

In addition to the above, you should have:

• 5+ years of hands-on experience implementing, configuring, optimizing, and supporting AVEVA PI System solutions in production environments.
• Strong expertise in Asset Framework, information modelling, Event Frames, PI Analytics, and scalable/reusable PI System data models.
• Experience leading complex implementations, upgrades, migrations, or performance optimization initiatives.
• Ability to independently troubleshoot complex technical issues in business-critical PI System environments.
• Experience acting as a Technical Lead or taking technical ownership of PI System projects.
• Ability to define and promote technical standards, implementation approaches, and best practices.
   

What we offer

• International projects in the industrial / operational technology domain.
• Opportunity to work with experienced PI System professionals and develop your expertise.
• A chance to grow towards a consulting or technical leadership role.
• Remote-first work environment.
• Challenging projects where industrial data and technology have a real business impact.
   

If you have strong AVEVA PI System experience and are looking for your next technical challenge, we’d be happy to hear from you.

We are looking for a Data Operations Manager to take ownership of the reliability, performance, and operational efficiency of our data platform.

We are also open to a strong Data Architect or Senior Data Engineer who has hands-on experience with data platform operations and is ready to take broader ownership of the data environment.

About the Role

You will be responsible for keeping our data platform reliable, scalable, observable, and cost-efficient. This is a hands-on role combining data engineering, platform operations, performance optimisation, data quality, and technical ownership.

You will work closely with Data Engineering, Analytics, BI, Security, and Finance teams to ensure that data is available, accurate, fresh, and delivered within agreed SLAs.

What You’ll Do

• Own and optimise our Snowflake data warehouse, including query performance, warehouse sizing, resource utilisation, and cost efficiency.
• Ensure the reliability and performance of dbt projects, covering data models, incremental processing, automated testing, documentation, and deployment.
• Oversee production ELT pipelines, ensuring data availability, freshness, reliability, and compliance with established SLAs.
• Build and improve monitoring, alerting, and incident management processes for the data platform.
• Lead root cause analysis and implement preventive measures to reduce recurring incidents.
• Establish and maintain data quality standards, automated validation, and observability practices across the platform.
• Manage data platform access controls, governance policies, and compliance requirements.
• Monitor Snowflake usage and credit consumption, identify optimisation opportunities, and contribute to platform budget planning.
• Develop operational runbooks, CI/CD workflows, and engineering best practices for data teams.
• Identify operational bottlenecks and continuously improve the reliability and efficiency of the data platform.
• Collaborate with Data Engineering, Analytics, BI, Security, and Finance teams on platform reliability, data quality, governance, and cost optimisation.

What We’re Looking For

• Extensive experience in Data Engineering, Analytics Engineering, DataOps, or Data Platform Operations.
• Strong hands-on experience with Snowflake administration, performance tuning, warehouse management, and SQL optimisation.
• Advanced experience with dbt Core or dbt Cloud, including incremental models, macros, testing, documentation, and deployment.
• Proven experience operating production ELT pipelines using Fivetran, Airbyte, Stitch, or similar tools.
• Strong understanding of DataOps practices, including CI/CD, Git/version control, automated testing, deployment pipelines, and production operations.
• Experience implementing data quality frameworks, monitoring, alerting, observability, and incident response.
• Good understanding of data governance, access management, security, and data platform reliability.
• Ability to troubleshoot complex data platform issues and drive them through to resolution.
• Strong ownership mindset and the ability to work independently across technical and operational areas.

Nice to Have

• Experience with data observability tools such as Monte Carlo, Elementary, or Datafold.
• Familiarity with workflow orchestration platforms such as Airflow, Dagster, or Prefect.
• Experience with cloud data platform cost optimisation, including Snowflake credit consumption and resource allocation.
• Knowledge of GDPR, SOC 2, or similar regulatory and compliance frameworks.
• Experience defining or improving data platform architecture and engineering standards.
• Previous experience acting as a Data Architect, Technical Lead, or Senior Data Engineer with ownership beyond individual pipelines or projects.

What Success Looks Like

In this role, success means having a data platform that is reliable, observable, predictable, and cost-efficient: pipelines run within SLAs, data quality issues are detected early, incidents are resolved systematically, Snowflake resources are used efficiently, and the engineering team has clear processes and tooling for operating the platform.

Level: Senior Engagement: Long-term contract Location: Europe (EU / EEA / UK), remote Working hours:EU business hours, shifted 2–3 hours later to ensure daily overlap with US West Coast (PST/PDT) mornings

ABOUT THE CLIENT

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

ABOUT THE ROLE

We are looking for a Senior Software Engineer to design, build, and own significant components of the core query engine of a large-scale distributed data platform. You will lead work across query planning, optimization, and execution — driving architectural decisions, mentoring middle engineers, and pushing the platform's performance and scalability boundaries across all major clouds.

This is a systems-level, backend engineering role focused on distributed data processing internals — not application development or CRUD services.

RESPONSIBILITIES

• Architect, design, and implement significant components of the query engine — planner, optimizer, execution operators, memory management, and data access layers.
• Write and review high-quality, performance-critical code in Java and/or C++.
• Own end-to-end delivery of features from technical design through production rollout.
• Drive performance optimization work — profiling, benchmarking, and eliminating bottlenecks in latency- and throughput-sensitive paths.
• Integrate deeply with columnar formats, open table formats, and connectivity drivers.
• Mentor middle engineers, lead code reviews, and set technical standards for the team.
• Contribute to CI/CD architecture and quality processes across Jenkins, containerization, and multi-cloud deployment (GCP, AWS, Azure via Kubernetes / Docker).
• Debug complex, cross-layer issues spanning query planning, distributed execution, memory management, and I/O.
• Partner with US-based engineering leads on design reviews, architectural decisions, and roadmap execution.

REQUIRED QUALIFICATIONS

• Education: B.S. or M.S. in Computer Science, Computer Engineering, or a related technical field.
• Programming: Strong proficiency in Java or C++, with deep understanding of OOP, systems design, memory management, and concurrency. Candidates strong in both are especially valued.
• SQL & Data: Advanced SQL skills and strong understanding of query execution internals — planning, optimization, and operator implementation.
• Data processing systems experience: Demonstrated experience building or extending data processing systems — query engines, distributed databases, ETL/ELT frameworks, or analytical platforms.
• Engineering experience: 5+ years in backend/systems software engineering, with a track record of owning significant components.
• CI/CD & DevOps: Solid experience with Jenkins and modern engineering workflows.
• Containers & Orchestration: Strong working knowledge of Docker and Kubernetes.
• Cloud: Hands-on experience with at least one major cloud (GCP, AWS, or Azure); exposure to more than one is a strong plus.
• Version Control: Confident with Git / GitHub workflows and rigorous code review practices.
• English: Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team.
• Availability: Able to work EU hours with a 2–3 hour shift toward US West Coast time to ensure daily overlap with the client team.
• Leadership: Prior experience mentoring engineers, leading technical designs, or owning components end-to-end.

DESIRED SKILLS

• Deep experience with Apache Arrow (columnar in-memory format).
• Hands-on experience with Apache Calcite (SQL parsing, planning, optimization framework).
• Experience with Gandiva or LLVM-based expression compilation and code generation.
• Experience with open table formats — Apache Iceberg, Delta Lake, or Hudi.
• Deep familiarity with distributed computing frameworks (e.g., Apache Spark, Kafka) and MPP SQL query engines (e.g., Presto, Trino, or similar).
• Deep understanding of query planning, optimization (cost-based, rule-based), and execution internals.
• Experience with data connectivity drivers: JDBC, ODBC, Arrow Flight.
• Kubernetes on managed services (GKE / EKS / AKS) and multi-cloud exposure.
• IaC tools such as Terraform.
• Track record of performance optimization at the systems level — profiling, vectorization, cache/memory-conscious design.
• Prior experience contributing to open-source data infrastructure projects.
• Please note: We can consider only candidates currently residing in the EU, EEA, or the UK

About Our Client

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

About the Role

We are looking for a Middle-level Software Engineer to work on the core query engine of a large-scale distributed data platform. You will develop features across query planning, optimization, and execution, contribute to performance-critical components, and help investigate and fix production issues reported by real enterprise customers.

This is a systems-level, backend engineering role focused on distributed data processing internals — not application development or CRUD services.

Responsibilities

• Develop and maintain features across the query engine — planning, optimization, execution, and data access layers
• Write performance-conscious code in Java and/or C++
• Investigate and fix production and customer-reported issues under the guidance of senior engineers, including fixes delivered across multiple supported release lines
• Work with SQL semantics, query plans, and execution operators over large-scale distributed data
• Integrate with columnar formats, open table formats, and connectivity drivers
• Contribute to CI/CD and automated testing in Jenkins
• Deploy and validate changes on Kubernetes (GKE/EKS/AKS) across GCP, AWS, or Azure with Docker
• Debug issues across query planning, distributed execution, memory management, and I/O; collaborate with US-based teams on design and code reviews

Required Qualifications

• B.S. or M.S. in Computer Science, Computer Engineering, or a related field
• 3+ years in backend / systems software engineering
• Strong proficiency in Java or C++ with solid OOP and software design fundamentals, including concurrency and asynchronous programming (comfort with both languages is especially valued)
• Strong SQL and understanding of relational and analytical data systems, including query execution concepts
• Hands-on experience with data processing systems: query engines, distributed databases, ETL/ELT, or analytical platforms
• Experience with Jenkins pipelines and modern development workflows
• Docker and basic Kubernetes (running workloads, debugging pods, kubectl fluency)
• Hands-on experience with at least one major cloud (GCP, AWS, or Azure)
• Confident Git/GitHub workflows
• Comfortable with AI-assisted development workflows — using modern AI tools for code comprehension, debugging, and test generation, and critically validating their output
• English Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team
• Availability to work EU business hours shifted 2–3 hours later for daily overlap with US West Coast mornings

Desired Skills

• Apache Arrow (columnar in-memory format) and SQL planner/optimizer frameworks such as Apache Calcite
• LLVM-based runtime expression compilation
• Open table formats — Apache Iceberg, Delta Lake, or Hudi — and columnar file formats such as Parquet, Avro, or ORC
• MPP query engines (Presto, Trino, or similar); exposure to distributed data platforms such as Spark, Snowflake, or Databricks
• Messaging systems: Kafka, NATS, or cloud pub/sub services
• Query planning, optimization, and execution internals
• Connectivity drivers: JDBC, ODBC, Arrow Flight
• Managed Kubernetes (GKE/EKS/AKS), multi-cloud exposure; Terraform
• Performance profiling of latency-sensitive systems

Details

• Engagement: Long-term contract
• Location: Ukraine, remote

About Our Client

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

About the Role

We are looking for a Senior Software Engineer to work on the core query engine of a large-scale distributed data platform. You will own significant components across query planning, optimization, and execution — both driving improvements and acting as a trusted owner of production quality: diagnosing complex customer-reported issues and delivering safe, well-tested fixes across supported release lines. Engineers with staff-level scope and ambitions are very welcome.

This is a systems-level, backend engineering role focused on distributed data processing internals — not application development or CRUD services.

Responsibilities

• Architect, design, and implement query engine components — planner, optimizer, execution operators, memory management, and data access layers
• Diagnose and resolve complex production and customer-reported issues across the full query path — correctness, performance, memory, concurrency, resiliency, and compatibility
• Develop, review, and backport fixes across multiple supported release lines; strengthen regression prevention
• Write and review high-quality, performance-critical code in Java and/or C++
• Analyze query profiles, execution plans, logs, and telemetry to drive performance optimization — profiling, benchmarking, and eliminating bottlenecks in latency- and throughput-sensitive paths
• Integrate with columnar formats, open table formats, and connectivity drivers
• Improve regression tests, diagnostics, and observability; contribute to CI/CD architecture and quality processes in Jenkins
• Mentor middle engineers, lead code reviews, and help set technical standards
• Debug complex cross-layer issues spanning query planning, distributed execution, memory management, and I/O, partnering with US-based engineering leads

Required Qualifications

• B.S. or M.S. in Computer Science, Computer Engineering, or a related field
• 5+ years in backend / systems software engineering with a track record of owning significant components end-to-end
• Strong proficiency in Java or C++ (comfort with both is especially valued), with deep understanding of OOP, systems design, memory management, concurrency, and asynchronous programming
• Advanced SQL and solid understanding of query execution internals — planning, optimization, operator implementation
• Experience building or extending data processing systems: query engines, distributed databases, ETL/ELT, or analytical platforms
• Ability to assess change risk in a mature production system; experience supporting software across multiple versions, branches, or customer environments
• Hands-on experience with Jenkins and modern CI/CD workflows; Docker and Kubernetes
• Hands-on experience with at least one major cloud (GCP, AWS, or Azure)
• Confident Git/GitHub workflows and rigorous code-review habits
• Comfortable with AI-assisted development workflows — using modern AI tools for code comprehension, debugging, and test generation, and critically validating their output
• Prior experience mentoring engineers, leading technical designs, or owning components end-to-end
• English Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team
• Availability to work EU business hours shifted 2–3 hours later for daily overlap with US West Coast mornings

Desired Skills

• Apache Arrow (columnar in-memory format) and SQL planner/optimizer frameworks such as Apache Calcite
• LLVM-based runtime expression compilation / code generation
• Open table formats — Apache Iceberg, Delta Lake, or Hudi — and columnar file formats such as Parquet, Avro, or ORC
• MPP query engines (Presto, Trino, or similar); background with distributed data platforms such as Spark, Snowflake, or Databricks
• Deep understanding of query optimization (cost-based, rule-based), vectorized execution, memory management, spilling, and caching
• Connectivity drivers: JDBC, ODBC, Arrow Flight
• Managed Kubernetes (GKE/EKS/AKS) and multi-cloud exposure; Terraform; cloud object storage (AWS S3, Azure Data Lake Storage, Google Cloud Storage)
• SaaS operations experience: incident response, profiling, observability, release and backport processes
• Systems-level performance work and open-source contributions to data infrastructure projects

Details

• Engagement: Long-term contract
• Location: Europe (EU / EEA / UK), remote
• Working hours: EU business hours, shifted 2–3 hours later for daily overlap with the US West Coast team

We are looking for a senior technical consultant with hands-on experience in mobile traffic infrastructure, backend routing, attribution, Keitaro, GEO segmentation, and anti-abuse systems.

Our current architecture is:

iOS App → AWS ECS / ALB → API → Keitaro

We need an expert to review the existing setup and help us design a reliable production-ready traffic routing and filtering architecture.

The main goal is to understand which traffic signals are realistically available within this stack, which of them are worth using, and how to structure the decision logic between the mobile app, backend, AWS infrastructure, and Keitaro.

Areas we would like to review include:

— IPv4 / IPv6
— GEO location and country-level routing
— ISP / ASN data
— VPN / proxy detection
— User-Agent
— Referrer data
— DNS-related signals
— suspicious or known-abuse IP databases
— request-rate limits and anti-bot / anti-abuse rules
— traffic attribution between different apps, sources, GEOs, and offers
— Keitaro tracking parameters, click IDs, subIDs, and postbacks
— offer routing, weighted distribution, caps, and fallback logic

We would also like an assessment of more advanced identification signals, including:

— device fingerprinting
— device-level signals available on iOS
— battery-related or environment signals
— third-party detection APIs / SDKs

We want to understand which of these signals are technically available on iOS, which require changes in the mobile application, what privacy or reliability limitations they have, and whether the additional complexity is justified.

Expected deliverables

We are initially looking for a technical consultation and architecture review, not immediate full-scale development.

We expect the specialist to provide:

— review of our current architecture;
— recommended traffic routing and filtering logic;
— recommended data sources, APIs, or services;
— assessment of accuracy and false-positive risks;
— expected infrastructure and third-party API costs;
— recommendations for Keitaro integration and attribution;
— QA / staging approach for testing the complete flow;
— clear implementation plan for our existing AWS + Keitaro infrastructure.

Preferred experience

Strong practical experience with several of the following is important:

— iOS mobile traffic infrastructure
— backend traffic routing
— AWS ECS / ALB
— Keitaro or similar tracking platforms
— mobile attribution
— server-to-server postbacks
— GEO / ASN / VPN / proxy detection
— anti-bot and anti-abuse systems
— affiliate traffic infrastructure
— high-volume traffic systems

Experience with real production mobile affiliate projects is highly preferred.

Please include a short description of similar systems you have worked with and explain what parts of this stack you personally designed or implemented.

We are open to starting with a paid 1–2 hour technical consultation.

We are looking for a Data  Engineer to own the reliability and performance of our data platform. You’ll collaborate with engineering and business teams to ensure a reliable, scalable data environment.

Responsibilities

• Manage and optimise the Snowflake data warehouse, including query performance, warehouse sizing, resource utilisation, and cost efficiency.
• Maintain the reliability and performance of dbt projects, including data models, automated testing, documentation, and deployment processes.
• Oversee ELT pipelines, ensuring data availability, freshness, and compliance with established SLAs.
• Implement monitoring, alerting, and incident management processes, including root cause analysis and preventive measures.
• Establish data quality standards, automated validation, and observability practices across the data platform.
• Manage access controls, data governance policies, and compliance requirements.
• Monitor Snowflake consumption, implement cost optimisation strategies, and support platform budget planning.
• Develop operational runbooks, CI/CD workflows, and best practices for data engineering teams.
• Collaborate with Data Engineering, Analytics, BI, Security, and Finance teams to improve platform reliability and operational efficiency.

Requirements

• Extensive experience in Data Engineering, Analytics Engineering, DataOps, or Data Platform Operations.
• Strong hands-on experience with Snowflake administration, performance tuning, warehouse management, and SQL optimisation.
• Advanced proficiency in dbt Core or dbt Cloud, including incremental models, macros, testing, and deployment.
• Proven experience managing production ELT pipelines using Fivetran, Airbyte, Stitch, or similar solutions.
• Solid understanding of DataOps principles, CI/CD, version control, automated testing, and deployment pipelines.
• Experience implementing data quality frameworks, monitoring, alerting, and incident response processes.
• Understanding of data governance, access management, and data platform reliability.

Nice to Have

• Experience with data observability tools such as Monte Carlo, Elementary, or Datafold.
• Familiarity with workflow orchestration platforms such as Airflow, Dagster, or Prefect.
• Experience optimising cloud data platform costs, including Snowflake credit consumption and resource allocation.
• Knowledge of regulatory and compliance frameworks, including GDPR and SOC 2.

About the role
We are looking for an Agile, product-oriented Senior Analytics Engineer to drive our
modern data architecture. You will be the bridge between raw data and business value for
our digital products and online services.
In this role, speed and value are key. You will adopt an MVP (Minimum Viable Product)
mindset to launch data products quickly, iterate fast, and provide actionable insights for our
Marketing and Finance teams. You will work on our cutting-edge stack—building robust
pipelines in dbt and BigQuery, and exposing data through our next-generation semantic
layer in Omni Analytics.

What you will do
● Modern Data Modeling: Design, build, and maintain scalable dbt models in
BigQuery. Transform raw data into clean, tested, and documented data marts ready
for analysis.
● MVP Delivery: Adopt a fast-paced, product-oriented mindset. Launch Minimum
Viable Products for data reporting to give product and business stakeholders quick
answers, then iterate based on feedback.
● Semantic Layer Ownership: Implement and govern the business logic in Omni
Analytics (or similar modern BI tools), ensuring metrics are defined once and trusted
by everyone.
● Cross-Functional Analytics: Collaborate closely with Marketing and Finance
departments to understand our digital platform metrics (CAC, LTV, ROI, conversion
funnels, retention, and MAUs) and translate them into technical solutions.
● Quality & CI/CD: Apply software engineering best practices to data: version control
(Git), automated testing, and CI/CD workflows for data pipelines.

Requirements
● Experience: 6+ years in a Data Engineering, Analytics Engineering, or advanced BI
role.
● Modern Data Stack (Essential): Extensive experience with dbt and cloud data
warehouses (preferably BigQuery, though Snowflake/Redshift is valid).
● BI & Semantic Layers: Experience with Omni Analytics is highly desired.
Alternatively, strong experience in Looker (LookML), Power BI, or Tableau, with a
clear understanding of semantic layers and governed metrics.
● Domain Knowledge: Strong understanding of digital products, online services, and
high-traffic platform dynamics. Exposure to Marketing and Finance data is highly
desirable.
● Technical Skills: Advanced SQL optimization, data modeling (star schema, OBT),
and Git workflow.
● Mindset: MVP-oriented, proactive, and capable of finding innovative solutions to
challenging problems.

Tools you will work with
BigQuery · dbt · Omni Analytics · SQL · Git · Digital product analytics platforms

Why join us?
 

🌍 Remote-first & flexible work

• Fully remote working model.
• Access to an office if there is one available in your location.
• Monday–Friday, 40-hour working week.
• Flexible working hours: schedule options between 08:00 and 19:00, including a 1-hour lunch break (the only requirement is a minimum of 5 hours of overlap with the team)

🏖️ Vacation & sick leave

• 15 paid working days of vacation during the first year.
• 20 working days after 2 years.
• 25 working days after 3 years.
• 10 paid sick days per year (for childcare would also be covered), compensated at 100% of salary.
• Public holidays are days off by default.

❤️ Health & family benefits

• Medical insurance or medical insurance compensation.
• Company gifts for:
  • Birthday
  • Wedding
  • Birth of a child
• Paid lunches at the office.
• Family days etc.

📑 FOP / Individual Entrepreneur Support if needed
🔄 Paid Bench period between projects
🤝 No time-tracking software — transparent and trust-based time reporting.
🎁 Referral Program

We’re looking for someone who will take full ownership of the technical function within our Analytics department — and bring Reporting, Data Tech, and AI to a new level of maturity.

The mission is clear: transform Reporting and Data Tech into a powerful internal platform that helps the entire analytics function work faster, smarter, and more effectively.

 

𝗪𝗵𝗮𝘁 𝘆𝗼𝘂’𝗹𝗹 𝗼𝘄𝗻:

▸ Build and scale BI infrastructure from the ground up

▸ Lead and develop teams across Reporting, Data Tech, and AI

▸ Design and implement scalable Data Warehouse architecture

▸ Own ETL/ELT processes and Data Governance frameworks

▸ Drive automation across reporting and data pipelines

▸ Integrate AI/LLM tools to optimize analytical processes

▸ Act as the bridge between technical analytics and business stakeholders

▸ Drive change management and build a culture of data-driven decisions

 

𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗯𝗿𝗶𝗻𝗴:

▸ 5+ years in BI / Data / Analytics

▸ Proven experience managing BI or Data teams

▸ Hands-on experience building BI functions from scratch

▸ Deep expertise in Data Warehousing and modern BI platforms

▸ SQL at expert level

▸ Strong understanding of ETL/ELT and Reporting infrastructure

▸ Practical experience with AI/LLM for process optimization

▸ Experience in change management

▸ Strong leadership, ownership mindset, and business orientation

 

𝗪𝗼𝘂𝗹𝗱 𝗯𝗲 𝗮 𝗽𝗹𝘂𝘀:

▸ iGaming background

▸ Experience in product companies

▸ Experience working with large-scale data volumes

▸ Familiarity with modern AI tools and frameworks

 

Our Benefit Cafeteria is Packed with Goodies:

• Children Allowance
• Mental Health Support
• Sport Activities
• Language Courses
• Automotive Services
• Veterinary Services
• Home Office Setup Assistance
• Dental Services
• Books and Stationery
• Training Compensation
• And yes, even Massage!
   

Ready to Take the Leap?

Join a team where every day is an opportunity to take a RISK and come out on top. Follow us on LinkedIn and Instagram, and let us be your hub for turning ambitious ideas and extraordinary solutions into reality.

At RISK, we believe that our people are the driving force behind our success. Together, we can achieve great things and push the boundaries of what’s possible in the iGaming industry.

N-iX is looking for a Middle Data Engineer for 6 months' engagement. 

You will act as the independent hands-on engineer and the technical owner of data readiness. The role combines pipeline and query engineering with investigation of source, mapping, hierarchy, and validation issues; coordination of market and provider validation cycles; reconciliation of outputs; and delivery of technically ready datasets to downstream consumers.

The Data Engineering team builds and maintains pipelines, while mappings and configurations determine cell/model scope; Snowflake extraction jobs run SQL through Databricks, with Blob Storage, validation, Medallion transformations, Power BI review, and Gold delivery to Ekimetrics. The operating model also requires end-to-end triage across MDFA, CDF, WPP, Data Foundation, Redmill, and market stakeholders rather than treating every discrepancy as a coding defect.

Key responsibilities:

• Independently design, develop, maintain, test, deploy, and optimize ingestion, validation, transformation, aggregation, data-quality, anomaly-detection, and extraction workflows
• Develop and tune Python/PySpark packages, SQL extraction queries, Databricks jobs, ADF pipelines, Delta tables, and source-to-target contracts
• Own technical readiness for new cells and refreshes: clarify filters and expected scope, implement or update queries, review mappings/configuration, reconcile outputs, and confirm readiness for business validation
• Investigate source, NCID, taxonomy, hierarchy, mapping, naming-convention, aggregation, missing-week, outlier, and performance issues across CDF, MDFA/PFME, APIs, manual files, and specific sources
• Coordinate technical validation cycles with Product, CMIA/markets, WPP/MDFA, CDF, other data providers, and Ekimetrics; convert reported discrepancies into actionable owners and technical evidence
• Drive complex incident resolution across pipelines, data contracts, access, service principals, secrets, networking, Power BI refreshes, and downstream delivery
• Review pull requests and test evidence; guide the Junior engineer and delegate scoped engineering/support work without becoming a people manager
• Maintain architecture documentation, interface contracts, repository documentation, runbooks, deployment procedures, and support/escalation guidance

• Recommend practical automation, reliability, performance, and maintainability improvements while respecting platform standards and business-validation ownership.

   

Must-have technical competencies:

• 5–6 years’ professional engineering experience with independent production ownership
• Strong Python, PySpark, and SQL, including complex transformations, query optimization, reusable packages, debugging, tests, and reconciliation
• Strong ETL/ELT and batch-pipeline engineering across relational warehouses, APIs, object storage, and file-based ingestion
• Experience with a cloud data lake/lakehouse, Medallion patterns, schema/interface contracts, data-quality controls, orchestration, observability, and incident recovery
• Solid Git engineering practices: branching, pull requests, reviews, automated tests, deployment controls, and documentation
• Proven ability to translate business/data requirements into filters, mappings, transformations, validation rules, and operational workflows

• Stakeholder-facing problem solving: explain discrepancies, challenge incomplete requirements, establish technical owners, and drive issues to closure.

   

Nice-to-have:

• Strong preference for Azure Data Factory, Azure Databricks, Databricks Jobs/API, Delta Lake, Snowflake, Azure Blob Storage/Data Lake, Power BI, GitHub, and Azure Key Vault
• Valuable experience with Pandera or equivalent schema-validation frameworks, Streamlit, Managed Identity/service principals, Azure Communication Services, Managed VNet/Private Endpoints, and Dev/Prod release practices
• Experience with PFME/media data, syndicated sales, marketing hierarchies, MMM inputs, or multi-market data onboarding is preferred but can be learned.

N-iX is looking for a Junior Data Engineer to join a data-focused team supporting the day-to-day operations and ongoing development of enterprise data solutions. This is a 6-month engagement with an opportunity to gain hands-on experience working with modern cloud data platforms, large-scale datasets, and production data pipelines.

You will work closely with experienced Data Engineers, contributing to pipeline maintenance, troubleshooting, onboarding new data sources, and implementing well-defined enhancements across the data platform. This role is ideal for engineers with 1-2 years of professional experience who want to deepen their expertise in data engineering and cloud technologies.

About the Role

As a Junior Data Engineer, you will help ensure the reliability and quality of data pipelines that support business-critical analytics and reporting. You'll participate in monitoring, troubleshooting, testing, and implementing data processing logic while following established engineering standards and delivery practices.

Under the guidance of senior team members, you will contribute to data ingestion processes, validation activities, technical documentation, and operational support, gaining exposure to modern technologies such as Azure, Databricks, PySpark, and Snowflake.

Responsibilities

• Monitor and support scheduled and event-driven data pipelines, data processing jobs, reporting refreshes, and data quality checks.
• Execute standard data ingestion and refresh activities from multiple sources, including APIs, CSV files, syndicated datasets, and configuration inputs.
• Implement well-defined Python, PySpark, and SQL changes, including new filters, transformations, mappings, validation rules, and minor pipeline enhancements.
• Perform schema validation, reconciliation checks, regression testing, and source-to-target data verification.
• Investigate pipeline failures, validation issues, missing data, rejected files, and reporting discrepancies.
• Troubleshoot first-line operational incidents using logs, job outputs, and monitoring tools.
• Escalate complex technical or business-data issues with clear diagnostic information and supporting evidence.
• Maintain technical documentation, deployment notes, runbooks, configuration files, and operational records.
• Follow established engineering practices, including Git workflows, pull requests, code reviews, testing, and deployment procedures.

• Support onboarding of new data sources and contribute to continuous improvement initiatives.

   

Requirements

• 1-2 years of professional experience in Data Engineering, ETL/ELT development, Analytics Engineering, or Data Support roles.
• Good working knowledge of Python and SQL.
• Understanding of data pipelines, data transformations, batch processing, and ETL/ELT concepts.
• Familiarity with relational databases and data warehouse concepts.
• Experience working with API integrations and file-based data processing (CSV, Parquet, etc.).
• Hands-on experience with Git, version control workflows, pull requests, and code reviews.
• Understanding of data quality principles, including validation, reconciliation, schema checks, and error handling.
• Ability to analyze logs, troubleshoot issues, and communicate findings clearly.
• Strong analytical and problem-solving skills.

• Intermediate or higher English level

  .

Nice to Have

• Experience with Azure Data Factory, Azure Databricks, PySpark, Snowflake, Delta Lake, Azure Data Lake Storage, or similar cloud data technologies.
• Exposure to modern data platform architectures, including Medallion (Bronze-Silver-Gold) data processing patterns.
• Familiarity with Power BI or other BI/reporting platforms.
• Knowledge of cloud environments and DevOps practices.
• Experience with data validation frameworks such as Pandera.

• Understanding of CI/CD pipelines and deployment processes.

   

What Makes You a Great Fit

• You enjoy working with data and solving technical problems.
• You are detail-oriented and take ownership of the quality of your work.
• You are comfortable working within established processes while continuously learning new technologies.
• You communicate clearly and collaborate effectively with technical and non-technical stakeholders.
• You are proactive, curious, and eager to grow your data engineering skills in a production environment.

N-iX is looking for a Middle Data engineer for 6 months' engagement. 

You will act as the independent hands-on engineer and the technical owner of data readiness. The role combines pipeline and query engineering with investigation of source, mapping, hierarchy, and validation issues; coordination of market and provider validation cycles; reconciliation of outputs; and delivery of technically ready datasets to downstream consumers.

The Data Engineering team builds and maintains pipelines, while mappings and configurations determine cell/model scope; Snowflake extraction jobs run SQL through Databricks, with Blob Storage, validation, Medallion transformations, Power BI review, and Gold delivery to Ekimetrics. The operating model also requires end-to-end triage across MDFA, CDF, WPP, Data Foundation, Redmill, and market stakeholders rather than treating every discrepancy as a coding defect.

Key responsibilities:

• Independently design, develop, maintain, test, deploy, and optimize ingestion, validation, transformation, aggregation, data-quality, anomaly-detection, and extraction workflows
• Develop and tune Python/PySpark packages, SQL extraction queries, Databricks jobs, ADF pipelines, Delta tables, and source-to-target contracts
• Own technical readiness for new cells and refreshes: clarify filters and expected scope, implement or update queries, review mappings/configuration, reconcile outputs, and confirm readiness for business validation
• Investigate source, NCID, taxonomy, hierarchy, mapping, naming-convention, aggregation, missing-week, outlier, and performance issues across CDF, MDFA/PFME, APIs, manual files, and specific sources
• Coordinate technical validation cycles with Product, CMIA/markets, WPP/MDFA, CDF, other data providers, and Ekimetrics; convert reported discrepancies into actionable owners and technical evidence
• Drive complex incident resolution across pipelines, data contracts, access, service principals, secrets, networking, Power BI refreshes, and downstream delivery
• Review pull requests and test evidence; guide the Junior engineer and delegate scoped engineering/support work without becoming a people manager
• Maintain architecture documentation, interface contracts, repository documentation, runbooks, deployment procedures, and support/escalation guidance
• Recommend practical automation, reliability, performance, and maintainability improvements while respecting platform standards and business-validation ownership.

Must-have technical competencies:

• 5–6 years’ professional engineering experience with independent production ownership
• Strong Python, PySpark, and SQL, including complex transformations, query optimization, reusable packages, debugging, tests, and reconciliation
• Strong ETL/ELT and batch-pipeline engineering across relational warehouses, APIs, object storage, and file-based ingestion
• Experience with a cloud data lake/lakehouse, Medallion patterns, schema/interface contracts, data-quality controls, orchestration, observability, and incident recovery
• Solid Git engineering practices: branching, pull requests, reviews, automated tests, deployment controls, and documentation
• Proven ability to translate business/data requirements into filters, mappings, transformations, validation rules, and operational workflows
• Stakeholder-facing problem solving: explain discrepancies, challenge incomplete requirements, establish technical owners, and drive issues to closure.

Nice-to-have:

• Strong preference for Azure Data Factory, Azure Databricks, Databricks Jobs/API, Delta Lake, Snowflake, Azure Blob Storage/Data Lake, Power BI, GitHub, and Azure Key Vault
• Valuable experience with Pandera or equivalent schema-validation frameworks, Streamlit, Managed Identity/service principals, Azure Communication Services, Managed VNet/Private Endpoints, and Dev/Prod release practices
• Experience with PFME/media data, syndicated sales, marketing hierarchies, MMM inputs, or multi-market data onboarding is preferred but can be learned.

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers

We are looking for a Lead Platform Engineer to lead the team behind the Equals 5 web platform, the Laravel + Vue application that pharma marketing teams use to run and measure their campaigns. This is a technical management role: you lead a small team of developers and a QA engineer, and you keep writing code yourself. You propose how a feature should be built, write it down as an FRD, agree it with the CTO, and then take it through review, release and whatever it does in production. You report to the CTO and work day to day with our product lead, who decides what we build and why.

Requirements

General

• 2+ years leading engineers as a tech lead, team lead or hands-on engineering manager: task breakdown, code review, mentoring, 1:1s
• 5+ years building and shipping web products as a full-stack engineer
• Experience carrying a whole product in production: releases, incidents, data correctness
• Comfortable in a fully remote team with hours overlapping EU timezones
• English B2 or above. Most of our communication is written and async

Technical

• Laravel in depth: Eloquent, queues, events, service providers, testing. We run Laravel 12 on PHP 8.3, with queues on Redis
• Vue: the SPA is Vue 2.7 with the Options API, Vuex and Vue Router, and there is no Composition API anywhere in it. Either Vue 2 or Vue 3 experience counts, as long as you can work inside an existing codebase and improve it. The rest of the frontend is dated too (Vite 2, bootstrap-vue 2, chart.js 2, vee-validate 2), so this is a frontend platform question rather than a framework upgrade. What we do about it, and when, is one of the first calls we will ask you to make with us
• PostgreSQL beyond CRUD: indexing, query plans, migrations against live tables. Production is Cloud SQL 14 while local and CI run 17, so a major-version upgrade is on the table
• Real SQL ability plus hands-on work with an analytical store: ClickHouse, BigQuery, Snowflake or similar. Our largest tables run to 1.5 billion rows, and Apache Druid is still in the hot path behind several controllers, so there are three analytical stores to hold in your head
• Writing FRDs and keeping technical documentation current: requirements, architecture decisions, API contracts, data models. We do not have this discipline today, and establishing it is a core part of the role rather than a bonus
• GCP at the service-design level: Cloud Run, Cloud SQL, GCS. You answer for what you deploy
• Docker and CI. GitHub Actions is primary, on a mix of self-hosted and GitHub-hosted runners, and a legacy Bamboo pipeline survives as a manual fallback for production deploys
• Automated testing as a habit. A mature Cypress E2E suite of 135 specs gates every pull request; PHPUnit coverage across 819 PHP files and 206 migrations is uneven, and there are no frontend unit tests at all. Closing that gap is part of the job
• Production debugging with Sentry and Laravel and nginx logs. Observability is thin today and improving it is yours to drive

Soft

• You lead in practice: you split work for other people, review their code, unblock them, and say no when that is the right answer
• You write clearly in English: FRDs, decision records, review comments
• Analytical: you find the root cause before you propose a fix
• Task management: your board reflects what is actually happening, and you raise risks while there is still time to act on them
• Care with healthcare-professional data: access, consent, PII
   

AI baseline:

AI-assisted development is how we ship, not something we are piloting. Concretely, what we expect from day one:

• Daily use of AI coding agents (Claude Code, Cursor, Codex or similar): required, not “I tried it once”
• 50%+ of the code you ship is drafted by agents and reviewed by you line by line
• Able to run 2–4 agents in parallel on isolated branches or worktrees and merge the results yourself
• You maintain agent context for your projects: CLAUDE.md / AGENTS.md, custom commands, rules files. At least 1 such artifact you wrote yourself and keep current
• You automate at least 1 recurring routine per month with an agent: reports, migrations, release checks, test generation
• You can tell when the agent is wrong and throw the output away. Whatever ships, you are the engineer on the hook for it — we ask about this in the interview
   

Nice to have:

• Ad-tech or marketing analytics background: impressions, clicks, attribution windows, audience matching
• Healthcare or pharma domain, especially consent and PII handling
• Infrastructure as code with Pulumi or Terraform. Our infrastructure is in Pulumi, in a separate repository
• Experience migrating a Vue 2 codebase to Vue 3
• TypeScript. There is none in the frontend today. Whether we introduce it, where we start, and whether the cost is worth paying is a call for this role to make
• Apache Druid
• BigQuery and ClickHouse cost and performance tuning
   

Responsibilities:

• Lead a small team of 2–4 developers plus QA: task breakdown, code review, mentoring, input into hiring
• Stay hands-on: you write code yourself as well as distributing it
• Design how features are built (data model, API, UI, tests, rollout), write the FRD, and take it through review with the CTO before the work starts
• Keep technical documentation current after the feature ships: architecture, API contracts, data models
• Work directly with the product lead on weekly priorities
• Query and shape campaign data across PostgreSQL, BigQuery, ClickHouse and Druid, and keep dashboards fast
• Own releases and reliability: deploys across four production VMs with an NFS-shared release directory and automatic rollback, monitoring, alerting, incident response
• Look after the per-PR preview environments, where every pull request gets its own database clone and a routed environment
• Work with healthcare-professional data under our access, consent and PII rules
• Report to the CTO and agree architecture and technical standards with them
   

Tech stack:

PHP 8.3 / Laravel 12 · Vue 2.7, Vuex, Vite · PostgreSQL 14 in production (17 locally and in CI), Redis · ClickHouse, BigQuery, Apache Druid · GCP, Docker, GitHub Actions · Sentry · PHPUnit, Cypress · Claude Code

What we offer:
- 18 days PTO
- 5 days sick leave
- 11 US national holidays
- 50% of course price (if relevant to job)
- Laptop
 

Automat-it is an all-in AWS Premier Partner and Managed Services Provider specializing in the startup ecosystem. With over 800 customers and 500+ AWS certifications, Automat-it brings hands-on expertise in AI, DevOps, and FinOps to empower fast-paced startups to grow, deliver & win. Our customers save significant time-to-market and optimize their cloud performance and costs.

We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.

We’re looking for a Senior Data Engineer to join our growing Data & Analytics practice and build modern data solutions for our customers on AWS.

This is a highly hands-on engineering role. You’ll work across different customer environments and data challenges, from modernizing legacy pipelines and building lakehouse platforms to optimizing analytical workloads and owning smaller data platforms end-to-end.

You won’t be limited to one stack or one long-running product. One project may involve Spark and Apache Iceberg, another Redshift performance and data modeling, and another Kafka, OpenSearch, or a new data platform built from ingestion through analytics.

We’re looking for someone who can take ownership of a data engineering problem, make sound technical decisions, and deliver independently without needing constant direction.

📍 Work location: Ukraine, remote

If you are interested in this opportunity, please submit your CV in English.

Responsibilities

• Design, build, and optimize production data pipelines and data platforms on AWS.
• Build scalable data processing workloads using Spark/PySpark, Python, and SQL.
• Build and modernize data lakes and lakehouse architectures, working with technologies such as Amazon S3, Apache Iceberg, AWS Glue, and related data services.
• Design, troubleshoot, and optimize analytical workloads and data warehouses, including Amazon Redshift and other OLAP platforms.
• Modernize legacy and inefficient data workloads, including moving existing ETL/ELT processing to scalable distributed data architectures.
• Work with streaming and event-driven data pipelines using technologies such as Kafka, Kinesis Data Firehose, and related services.
• Investigate real production data problems, from performance bottlenecks and data modeling issues to upstream/downstream dependencies, and recommend practical solutions.
• Take ownership of customer projects end-to-end, from understanding the technical problem through implementation, testing, and delivery.
• Work directly with customers when needed to clarify requirements, explain technical decisions, and recommend solutions based on your expertise.
• Build data foundations for analytics and ML workloads, collaborating with Data Scientists, DevOps, and MLOps engineers.
• Contribute to dashboards and analytics when required using QuickSight or other BI tools.

• Apply practical engineering practices around data quality, testing, CI/CD, security, governance, and infrastructure automation.

   

Requirements

• Strong production experience in Data Engineering, with the ability to independently own and deliver data projects.
• Deep hands-on experience with Apache Spark / PySpark and distributed data processing.
• Strong Python and SQL skills.
• Hands-on experience building data solutions on AWS.
• Strong understanding of data lakes, lakehouse architectures, ETL/ELT, data modeling, and data processing at scale.
• Experience with analytical databases and data warehouses. Strong Amazon Redshift experience is highly valuable; deep experience with platforms such as Snowflake, Synapse, or similar OLAP technologies is also relevant.
• Understanding of streaming architectures and technologies such as Kafka or Amazon Kinesis.
• Ability to troubleshoot existing data systems, understand how data flows across upstream and downstream components, and improve performance and reliability.
• Ability to work autonomously, make pragmatic technical decisions, and take responsibility for delivery.

• Good communication skills and the ability to discuss technical problems and solutions directly with customers in English.

   

Nice to have

• Experience with Apache Iceberg or other modern open table formats and lakehouse technologies.
• Amazon OpenSearch Service / Elasticsearch experience.
• Experience with BI and visualization tools such as Amazon QuickSight, Tableau, or Power BI.
• Terraform and Infrastructure as Code.
• CI/CD practices for data pipelines, including data testing and validation.
• Experience preparing data platforms for ML workloads using services such as Amazon SageMaker.
• Exposure to Amazon Bedrock, GenAI, or agentic development.

• Experience with Databricks, Snowflake, ClickHouse, or other modern data platforms

   

What Success Looks Like in Year One

• Independently owns assigned data projects end-to-end, from understanding the problem and making technical decisions to implementation, troubleshooting, testing and successful delivery, with minimal supervision.
• Has built strong practical expertise across the team’s core data stack and can confidently work across different customer environments, including Spark, Python/SQL, data lakes/lakehouses, analytical and NoSQL databases, streaming and other technologies used by the team.
• Can investigate unfamiliar data problems independently, identify performance or data-modeling issues, understand upstream/downstream dependencies, and recommend pragmatic solutions to customers.

• Continuously expands technical depth, keeps up with modern Data technologies and patterns, and can quickly learn new technologies as customer projects and the Data practice evolve.

   

Benefits

• Professional training and certifications covered by the company (AWS, FinOps, Kubernetes, etc.)
• International work environment
• Referral program — enjoy cooperation with your colleagues and get a bonus
• Company events and social gatherings (happy hours, team events, knowledge sharing, etc.)
• English classes

• Soft skills training

   

Country-specific benefits will be discussed during the hiring process.

Automat-it is committed to fostering a workplace that promotes equal opportunities for all and believes that a diverse workforce is crucial to our success. Our recruitment decisions are based on your experience and skills, recognising the value you bring to our

Position Overview

We are looking for a Junior SRE / DevOps Engineer to join our infrastructure team and help support Kubernetes environments, CI/CD pipelines, application deployments, and production systems.

This role is a good fit for someone who already has basic hands-on experience with Linux, containers, Kubernetes, and CI/CD and wants to grow into an SRE / DevOps position.

Responsibilities

• Support Kubernetes-based environments.
• Assist with application deployments and CI/CD pipelines.
• Work with Jenkins pipelines.
• Support Java application build processes using Maven (mvn).
• Help troubleshoot deployment and infrastructure issues.
• Monitor applications and infrastructure.
• Work with logs, metrics, and alerts.
• Assist with infrastructure automation.
• Maintain technical documentation.
• Work with developers and senior DevOps/SRE engineers on production issues and improvements.

Required Skills

• Basic Linux administration skills.
• Basic understanding of Kubernetes.
• Understanding of Docker and containers.
• Basic experience with Jenkins or another CI/CD system.
• Basic understanding of Maven (mvn) and Java application build processes.
• Understanding of Git.
• Basic networking knowledge: DNS, TCP/IP, HTTP/HTTPS.
• Ability to read logs and troubleshoot basic application or infrastructure issues.
• Basic scripting skills with Bash or Python.
• Willingness to learn and work with production infrastructure.

Nice to Have

• Experience with Helm.
• Experience with Prometheus and Grafana.
• Basic knowledge of cloud platforms such as AWS, Azure, or GCP.
• Experience with Terraform or other Infrastructure-as-Code tools.
• Knowledge of Dremio.
• Basic understanding of Apache Arrow.
• Experience with PostgreSQL or object storage.
• Understanding of SRE concepts such as monitoring, alerting, availability, and incident response.

What We Expect

We do not expect a Junior engineer to know everything.

The important things are strong fundamentals, willingness to learn, and the ability to investigate problems instead of only following predefined instructions.

The engineer will work together with more experienced team members and gradually take more responsibility for CI/CD, Kubernetes, monitoring, and production infrastructure.