Jobs Data Engineer

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 for the team that owns the reliability and operational excellence of a large-scale data platform, delivered both as a cloud service and as self-managed software. You will work across the full platform — observability, security, control plane, and core services — diagnosing complex issues and delivering durable fixes across cloud, hybrid, and on-premises environments, in close collaboration with product engineering, SRE, support, and field teams.

This is a backend platform engineering role focused on distributed systems reliability — not application development or CRUD services.

Responsibilities

• Investigate, diagnose, and resolve complex issues across cloud and self-managed deployments — distributed systems failures, performance degradation, and reliability incidents
• Own bug fixes and targeted improvements across core services, control plane, security, and observability infrastructure
• Support automated deployment and integration frameworks that enable consistent, repeatable delivery across cloud, hybrid, and on-premises environments
• Contribute to cluster lifecycle management, observability, and operational supportability capabilities
• Partner with SRE, support, and field teams to triage and resolve escalated customer issues
• Build runbooks, root cause analyses, and internal tooling that reduce time-to-resolution over time
• Collaborate with product engineering so that fixes are integrated back into the main codebase effectively

Required Qualifications

• Bachelor's or Master's degree in Computer Science or a related field, or equivalent experience
• 5+ years of software engineering experience with strong hands-on Java development at production scale
• Proficiency in Python and Bash for scripting, automation, and tooling
• Solid foundation in data structures, algorithms, and multi-threaded / asynchronous programming models
• Experience designing, debugging, and operating large-scale distributed systems or microservices
• Hands-on experience with Kubernetes and Docker across cloud, hybrid, or on-premises deployments
• Cloud-native development experience on one or more of AWS, Azure, or GCP
• Strong understanding of networking fundamentals (TCP/IP, DNS, HTTP)
• Clear communicator — able to explain problems, root causes, and solutions to technical and non-technical audiences
• Comfortable with AI-assisted development workflows — using modern AI tools productively while 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

• Observability platforms: OpenTelemetry, distributed tracing, metrics, logging
• Background in data processing, data warehouse, data lake, or analytics systems
• Distributed file and object storage: S3, ADLS, or HDFS
• Infrastructure-as-code (Helm, Terraform) and CI/CD tooling (Jenkins, ArgoCD)
• Identity, authentication, and authorization systems: SSO, OAuth, SAML, RBAC
• NoSQL databases (MongoDB, RocksDB), Redis, or partner/OEM integration patterns
• Query processing, concurrency control, data replication, or storage systems

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

Project Description:

We are embarking on a critical initiative to achieve ISO 81001 compliance for our esteemed client. ISO 81001 is a pivotal standard for health informatics, specifically focusing on the management and security of health information systems. This project aims to ensure Client's digital infrastructure aligns with the stringent requirements set forth by ISO 81001, safeguarding sensitive healthcare data and bolstering cyber resilience.

Responsibilities:

Conduct a thorough assessment of Client's existing IT infrastructure, policies, and procedures against ISO 81001 requirements.
Identify gaps and vulnerabilities that need to be addressed to achieve compliance.
Collaborate with cross-functional teams to design and implement robust security controls aligned with ISO 81001 standards.
Deploy necessary security measures to protect Client's health information systems and data assets.
Draft and enhance security policies, procedures, and guidelines tailored to ISO 81001 specifications.
Ensure policies are communicated effectively and integrated into Client's operational framework.
Perform risk assessments and develop risk management strategies to mitigate cyber threats and vulnerabilities.
Implement proactive measures to enhance Client's cyber resilience in alignment with ISO 81001.
Conduct training sessions and workshops to enhance cyber security awareness among Client staff.
Foster a culture of security consciousness and best practices across the organization.
Establish monitoring mechanisms to track ongoing compliance with ISO 81001 requirements.
Conduct regular audits and assessments to ensure sustained adherence to security standards.
Develop incident response protocols and procedures to swiftly address and mitigate security incidents.
Continuously assess and refine security strategies based on emerging threats and industry developments.

Mandatory Skills Description:

Bachelor's degree in Cyber Security, Information Technology, or a related field.
Proven experience in cyber security, particularly in compliance initiatives (ISO standards preferred).
Strong understanding of health informatics and data security within healthcare environments.
Excellent analytical skills with the ability to assess complex systems and processes.
Effective communication skills to engage stakeholders and drive security initiatives forward.

Nice-to-Have Skills Description:

Agile

Languages:

English: C1 Advanced

Project Description:

Provide governed data, enterprise knowledge, document intelligence, knowledge graph, and retrieval capabilities that ground AI agents in trusted evidence

Responsibilities:

Design RAG and enterprise-search architectures.
Build ingestion, transformation, enrichment, document extraction, embedding, and indexing pipelines in Databricks.
Create and maintain Azure AI Search, vector search, and access-controlled retrieval indexes.
Implement Azure Document Intelligence pipelines for extracting structured data from PDFs, forms, engineering documents, reports, and scanned content.
Design and maintain knowledge graph models to represent entities, relationships, metadata, lineage, and domain context for agent reasoning and search enrichment.
Implement metadata, filtering, citations, lineage, data freshness, and security-trimmed access controls.
Measure and optimize retrieval relevance and knowledge quality.
Support structured data products used by agents and deterministic services.

Mandatory Skills Description:

Bachelor's degree in Computer Science, Data Engineering, Data Science, Information Systems, or a related field.
5+ years of data engineering, search engineering, analytics engineering, or knowledge-platform experience.
Strong SQL skills and experience building production data pipelines in Databricks.
Experience with data quality, metadata, lineage, and governance practices.
Understanding of structured and unstructured enterprise information retrieval, including document extraction and knowledge graph patterns.
Hands-on Azure AI Search, Databricks, RAG, or vector-search experience.
Experience with Azure Document Intelligence, document processing, chunking, embeddings, ranking, metadata, and citation design.
Hands-on knowledge graph experience using Neo4j, RDF, graph databases, semantic modeling, ontology design, or entity-relationship mapping.

Nice-to-Have Skills Description:

Azure Data Engineer or Databricks certification.

Languages:

English: B2 Upper Intermediate

N-iX is looking for a BI Engineer to join one of the biggest and strongest Data Units in Ukraine.

Our client is a US-based company that provides In-flight Internet and entertainment services to aircraft across the globe, serving commercial aircrafts. At this moment we cooperate on Business Intelligence, Data Analysis  and BigData directions and look for talents who can contribute to the complex data management and analysis projects.

Requirements:

• Proven hands-on experience with Tableau, including Tableau Desktop and Tableau Server
• Proven experience migrating BI reports and dashboards from Tableau to Power BI
• Hands-on experience with Power BI, including report and dashboard development
• Strong knowledge of SQL
• Experience with DAX and Power Query
• Knowledge of SQL Server Reporting Services (SSRS), SQL Server Integration Services (SSIS) and Paginated Reports
• Experience with Airflow
• Basic knowledge of Python
• Upper-intermediate English or higher

Responsibilities:

• Migrate existing BI reports and dashboards from Tableau to Power BI, ensuring that business logic, calculations, and reporting requirements are preserved.
• Analyze existing Tableau dashboards, reports, data sources, and business logic and translate them into Power BI solutions.
• Design, develop, and optimize Power BI reports and dashboards based on existing Tableau solutions and business requirements.
• Validate migrated reports against the original Tableau versions to ensure data accuracy, consistency, and functional parity.
• Convert business and information needs into effective, user-friendly reports and dashboards.
• Identify gaps, inconsistencies, and technical challenges during the migration process and propose appropriate solutions.
• Implement and optimize BI solutions to support reporting and analytical needs.
• Ensure the quality, accuracy, and performance of migrated and newly developed reports.

Nice to have:

• Experience with Snowflake, Databricks
• AI usage for Power BI report development and/or migration of reports between different BI Tools 

Project technology stack

The project currently utilizes a modern BI ecosystem centered around Tableau, SSRS, and Kibana for reporting and dashboarding, with data sourced from Snowflake. Data transformations are handled through DBT, while orchestration is supported via Airflow. As part of the BI modernization initiative, existing Tableau reporting solutions are being migrated to Power BI.

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

N-iX is looking for a Senior Data Engineer to support our client's Data Licensing team, which develops training datasets for frontier AI labs. You will own the onboarding and enrichment of new multimedia content. Extend scalable event-driven and batch-processing systems that produce reliable metadata for curation and customer-facing applications.

Responsibilities:

- Partner with the Principal Engineer, senior technical leaders and platform teams to implement and evolve the architectural direction for metadata-processing systems; independently own the design and delivery of assigned components.
- Evaluate and evolve orchestration for enrichment and backfill workloads, either by extending existing ingestion capabilities or integrating with new cloud-native services, while maintaining consistent contracts and lifecycle handling.
- Evolve shared frameworks so data scientists can package, test, validate and run production enrichment workloads with minimal data engineering support.
- Define and maintain metadata contracts, lineage, validation and quality controls.
- Contribute to and apply shared engineering standards, and provide technical guidance through design reviews, documentation and collaboration with data engineering and data science teams.
- Estimate and refine the cost, capacity and completion time of enrichment and backfill workloads.
- Partner with platform teams responsible for operating the underlying cloud infrastructure.
- Own time-sensitive customer and partner deliverables through production release.

Requirements:

- 8+ years of experience in data engineering, backend software engineering or distributed systems, including hands-on ownership of large-scale production data workloads.
- Demonstrated experience designing and operating large-scale ingestion or distributed data-processing systems.
- Strong Python software engineering and distributed processing experience.
- Practical knowledge of high-volume batch and event-driven architectures, including idempotency, replay, backpressure, retries and failure recovery.
- Experience enabling data science or ML workloads to move from prototype to production through reusable frameworks and engineering guardrails.
- Production experience with AWS and GCP, with the ability to design workflows spanning multiple cloud environments.
- Experience managing evolving data contracts, schemas, lineage and data-quality expectations.
- Ability to benchmark distributed workloads and translate results into credible capacity, execution-time and cloud-cost projections.
- Experience setting engineering standards, reviewing system designs and mentoring engineers or data scientists.
- Track record of delivering critical, time-sensitive systems into production.

Nice to have:

- Containerized CPU/GPU batch and ML workloads using managed compute services such as SageMaker, AWS Batch, ECS, Cloud Batch or GKE/EKS.
- Event-driven orchestration and messaging using services such as Step Functions, Lambda, EventBridge, Kinesis, Workflows or Pub/Sub.
- Snowflake, ClickHouse or Apache Iceberg.
- Production-scale image, video or audio processing

We are looking for a Platform Delivery Lead to join a cross-functional team working on a modern Data & AI platform for an international enterprise. This role is ideal for a delivery-focused professional who enjoys coordinating complex initiatives, aligning stakeholders, and ensuring operational excellence across platform environments.

As a key operational partner for platform leadership, you will drive backlog prioritization, release coordination, and delivery transparency while collaborating closely with business and engineering teams. The position is offered in a hybrid format based in Prague, Czechia.

We at Sigma Software create opportunities to work on impactful international projects, collaborate with experienced professionals, and grow within a people-oriented and engineering-driven environment.

Сustomer

Our Customer is an international technology and workforce solutions organization operating across multiple markets and industries. The company focuses on digital transformation initiatives and scalable platform capabilities that support operational efficiency, automation, and AI-driven decision-making across the business.

Project

The project focuses on the operational coordination and continuous delivery of a modern Data & AI platform that supports internal teams with scalable platform services and data-driven initiatives. The environment combines Agile and DevOps delivery practices with cloud-based technologies and platform engineering principles.

In this role, you will work at the intersection of business and technology, ensuring efficient backlog management, dependency coordination, release readiness, and alignment across multiple platform initiatives.

We are looking for a DevOps Engineer to join a team building advanced sports technology solutions that help unlock the full potential of athletes and teams, from wearable devices to large-scale analytics platforms.
 

You will own the infrastructure and delivery pipelines behind a high-load data platform, and work closely with backend and data engineers to keep it fast, observable, and reliable.
This role requires working in the Australian time zone (AEST/AEDT), which means a working day from approximately 01:00 to 10:00 (02:00 to 11:00)  Kyiv time

Responsibilities:

• Build, maintain, and improve CI/CD pipelines for backend and data services;
• Manage cloud infrastructure as code, including provisioning, scaling, and cost control;
• Own containerisation and orchestration: Docker images, Kubernetes workloads, deployment strategies;
• Set up and maintain monitoring, logging, and alerting so issues are caught before users see them;
• Improve reliability of a high-load distributed system: capacity planning, performance tuning, incident response;
• Automate routine operational work and reduce manual steps in the release process;
• Apply security best practices across infrastructure, secrets management, and access control;
• Collaborate with cross-functional teams in an Agile environment and take part in architecture discussions.

Requirements:

• 3+ years of experience in a DevOps, SRE, or infrastructure engineering role;
• Hands-on experience with a major cloud provider (AWS, GCP, or Azure);
• Strong experience with Docker and Kubernetes in production;
• Infrastructure as Code: Terraform or equivalent;
• Building and maintaining CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, or similar);
• Confident with Linux administration and scripting (Bash, Python, or Go);
• Monitoring and observability tooling: Prometheus, Grafana, ELK, Datadog, or similar;
• Understanding of networking, security, and access management fundamentals;
• English: Upper-Intermediate or higher;
• Availability to work in the Australian time zone (AEST / AEDT).

Nice to have:

• Experience with high-load or distributed systems;
• Experience supporting data-heavy platforms or streaming pipelines;
• Database administration experience (PostgreSQL or similar);
• Cost optimisation experience in cloud environments.

Conditions:

• Full remote;
• Full-time;
• Working hours aligned with the Australian time zone.

Important: after confirming your application on this platform, you’ll receive an email with the next step: completing your application on our internal site, LaunchPod. So keep an eye on your inbox and don’t miss this step — without it, the process can’t move forward.

Why join us

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you! :)

About the role

We are looking for a Data Engineer with strong Databricks and GCP experience to develop scalable ETL/ELT pipelines and productionize agentic workflows for data engineering automation.

The mandatory requirements are 5+ years of experience with Databricks and GCP, advanced SQL, and experience with Delta Lake and lakehouse architectures, along with an upper-intermediate English level. Experience building AI agents is a plus.

What you will do

• Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
• Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
• Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
• Develop data pipelines and processing solutions to support new business requirements and datasets.
• Build reusable frameworks and components that can be leveraged across multiple data engineering and business use cases.
• Implement data quality checks, monitoring, validation, exception handling, and production controls.
• Optimize PySpark and SQL workloads for performance, reliability, and scalability.
• Support testing, deployment, productionization, and ongoing enhancement of data and agentic solutions.
• Troubleshoot complex data and production issues and implement sustainable solutions.
• Collaborate with business, data engineering, and platform teams to identify further automation opportunities.

Must haves

• 5+ years of strong hands-on experience with Databricks and PySpark.
• Advanced SQL and data-processing skills.
• Hands-on experience with GCP, particularly BigQuery.
• Experience with Delta Lake and modern data lake/lakehouse architectures.
• Strong understanding of ETL/ELT, data pipeline design, performance optimization, and data quality.
• Experience building reliable, scalable, production-grade data solutions.
• Strong analytical and troubleshooting skills.
• Understanding of software engineering practices, including testing, version control, deployment, monitoring, and production support.
• Upper-intermediate English level.

Nice to haves

• Experience developing or integrating AI/agentic workflows, AI agents, or workflow automation solutions.
• Experience applying AI to automate data engineering, validation, troubleshooting, or operational processes.
• Familiarity with orchestration and automation frameworks.
• Experience developing reusable data engineering frameworks and platform components.
• Exposure to productionizing AI-enabled solutions with appropriate validation, monitoring, and human oversight.

Perks

• Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
• Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
• Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
• Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
• Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
• Well-being & support: access local well-being programs and people-focused support tailored to your location

Meet Our Recruitment Process

Asynchronous stage – An automated, self-paced track that helps us move faster and give you quicker feedback:

• Short online form to confirm basic requirements
• 30–60 minute skills assessment via Codility – a platform founded in Poland that helps us provide quicker feedback and streamline this stage of the process.
• 5-minute introduction video

Synchronous stage – Live interviews

• Technical interview with our engineering team (scheduled at your convenience)
• Final interview with your future teammates

If it’s a match — you’ll get an offer!

N-iX is looking for Senior/Lead Data Engineer to join the team

About the client

Our client is a leading European online car marketplace, connecting car buyers and sellers across the continent. The platform serves over 30 million monthly users and operates across 18 countries, making it one of the largest and most trusted digital marketplaces for vehicles in Europe.

About the role

We are looking for a Data / Analytics Engineer to support the client team. The role sits close to the data analysts and business stakeholders, with a primary focus on maintaining and extending the data pipelines and data models that power analytics and reporting. The core stack includes SQL, Airflow, Athena, AWS Data Lake, and AI-assisted engineering tools such as Codex.

The client is currently migrating away from DBT and Snowflake to the clients data platform. The destination cloud and data platform already exists and is operational, and the migration itself will be handled by a different team — so this is not an infrastructure or platform engineering role. Instead, we need an engineer who can understand analytics use cases, translate business requirements into robust data solutions, and work independently to get those solutions into production. Beyond the technical skills, we are looking for someone with a strong sense of ownership and engineering judgment: a person who does not simply execute a ticket or accept a blocker at face value, but actively clarifies unclear requirements, investigates problems, proposes alternatives when the obvious approach does not work, and involves the right people when decisions are needed.

Responsibilities:

• Maintain, troubleshoot, and improve existing data pipelines, and build new pipelines and datasets based on analytics and business requirements.
• Design and maintain analytical data models, including fact and dimension models.
• Ingest and integrate data from different source systems into the AWS Data Lake.
• Build, deploy, validate, monitor, and, when necessary, backfill data pipelines.
• Partner with data analysts to understand their requirements and provide reliable, easy-to-use datasets.
• Clarify requirements with business stakeholders rather than implementing ambiguous requests as-is.
• Ensure data quality through appropriate validation and testing, investigating data and pipeline issues and taking ownership of finding pragmatic solutions end to end.
• Use AI-assisted development tooling effectively while critically reviewing and validating generated solutions.

Requirements:

• Hands-on data engineering experience building and operating production data pipelines, ideally within analytics-driven environments.
• Strong SQL skills.
• Experience with data modeling for analytics, including fact and dimension tables.
• Experience with workflow orchestration, preferably Airflow.
• Experience working with AWS-based data environments, particularly Athena and AWS Data Lake.
• Experience with data validation, testing, and troubleshooting.
• Strong sense of ownership and engineering judgment: comfortable working with incomplete requirements, proactively seeking context, challenging assumptions when appropriate, and looking for practical solutions rather than stopping at the first blocker.
• Excellent verbal and written English skills, with clear communication across data analysts, engineers, and business stakeholders.

Nice to have

• Experience with data mesh architectures or working in a data mesh environment.
• Experience with marketplace, web/app, or digital analytics data.
• Experience with DBT and/or Snowflake.
• Familiarity with AI-assisted engineering tools such as Codex

N-iX is looking for Senior/Lead Data Engineer to join the team

About the client

Our client is a leading European online car marketplace, connecting car buyers and sellers across the continent. The platform serves over 30 million monthly users and operates across 18 countries, making it one of the largest and most trusted digital marketplaces for vehicles in Europe.

About the role

We are looking for a Data / Analytics Engineer to support the client team. The role sits close to the data analysts and business stakeholders, with a primary focus on maintaining and extending the data pipelines and data models that power analytics and reporting. The core stack includes SQL, Airflow, Athena, AWS Data Lake, and AI-assisted engineering tools such as Codex.

The client is currently migrating away from DBT and Snowflake to the clients data platform. The destination cloud and data platform already exists and is operational, and the migration itself will be handled by a different team — so this is not an infrastructure or platform engineering role. Instead, we need an engineer who can understand analytics use cases, translate business requirements into robust data solutions, and work independently to get those solutions into production. Beyond the technical skills, we are looking for someone with a strong sense of ownership and engineering judgment: a person who does not simply execute a ticket or accept a blocker at face value, but actively clarifies unclear requirements, investigates problems, proposes alternatives when the obvious approach does not work, and involves the right people when decisions are needed.

Responsibilities:

• Maintain, troubleshoot, and improve existing data pipelines, and build new pipelines and datasets based on analytics and business requirements.
• Design and maintain analytical data models, including fact and dimension models.
• Ingest and integrate data from different source systems into the AWS Data Lake.
• Build, deploy, validate, monitor, and, when necessary, backfill data pipelines.
• Partner with data analysts to understand their requirements and provide reliable, easy-to-use datasets.
• Clarify requirements with business stakeholders rather than implementing ambiguous requests as-is.
• Ensure data quality through appropriate validation and testing, investigating data and pipeline issues and taking ownership of finding pragmatic solutions end to end.
• Use AI-assisted development tooling effectively while critically reviewing and validating generated solutions.

Requirements:

• Hands-on data engineering experience building and operating production data pipelines, ideally within analytics-driven environments.
• Strong SQL skills.
• Experience with data modeling for analytics, including fact and dimension tables.
• Experience with workflow orchestration, preferably Airflow.
• Experience working with AWS-based data environments, particularly Athena and AWS Data Lake.
• Experience with data validation, testing, and troubleshooting.
• Strong sense of ownership and engineering judgment: comfortable working with incomplete requirements, proactively seeking context, challenging assumptions when appropriate, and looking for practical solutions rather than stopping at the first blocker.
• Excellent verbal and written English skills, with clear communication across data analysts, engineers, and business stakeholders.

Nice to have

• Experience with data mesh architectures or working in a data mesh environment.
• Experience with marketplace, web/app, or digital analytics data.
• Experience with DBT and/or Snowflake.
• Familiarity with AI-assisted engineering tools such as Codex

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.

We are a fully remote data analytics and BI agency, currently looking for a Data Engineer to build Python and PostgreSQL pipelines for client projects on Google Cloud Platform and Azure.

Read this part carefully: this is a part-time, project-based role with no guaranteed hours. You join our roster of specialists, and when data engineering work comes in, we assign it to you. Some months are busy, some are quiet. If you need a steady income from one employer, this role is not a good fit.

What you will do:
●Write production Python code for ETL / ELT pipelines
●Design PostgreSQL databases and tune them: schemas, indexes, query speed
●Build pipelines on Google Cloud Platform (BigQuery) or Azure, depending on the client
●Set up incremental loads, scheduling, logging, alerts, and backfills
●Prepare clean tables for Power BI and Looker Studio reports
●Write automated tests for Python pipelines
●Document pipelines, tables, and design decisions

Required Skills:
●Strong Python for data engineering: API calls, file processing, database connections, pandas
●PostgreSQL and SQL: schema design, indexing, query optimization
●Google Cloud Platform (BigQuery) or Azure (Azure SQL)
●Building ETL / ELT pipelines end-to-end, from source extraction to scheduled loads
●Incremental loads and handling schema changes in source data
●Data quality checks and pipeline troubleshooting
●Git / version control
●Clear documentation
●English skills (B1-B2), both written & spoken

Important! You must be able to showcase 2-4 past projects - ideally Python pipelines, PostgreSQL databases, or cloud data platforms you've built end-to-end. Be ready to walk us through the architecture, your Python and PostgreSQL design decisions, and the challenges you solved.

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.

We are seeking an experienced Data Engineer to develop and support reliable data infrastructure across our systems. In this role, you will create scalable data pipelines, maintain data accuracy and consistency, and connect external data sources. Your main goal will be to ensure smooth data movement and strengthen analytics capabilities throughout the organization. You will also help shape our data architecture, apply engineering best practices, and work closely with cross-functional teams to transform raw data into meaningful, actionable insights.

Requirements

• 3+ years of commercial experience in Data Engineering
• Experience gathering business requirements and communicating with stakeholders
• Strong knowledge of Data Warehouse (DWH) architecture and principles
• Hands-on experience designing data warehouses and building ETL pipelines
• Deep practical experience with Python
• Hands-on experience in one or more of the following areas: data processing using PySpark, Apache Spark, Pandas, or similar frameworks; data scraping; integration of third-party data sources and APIs
• Proficiency in SQL and relational databases such as PostgreSQL and MySQL
• Hands-on experience with column-oriented databases such ClickHouse, Snowflake, Cassandra, BigQuery (many tables with millions of records and above each)
• Solid understanding of software design patterns, algorithms, and data structures
• Intermediate English proficiency

Will be a plus

• Experience with messaging platforms such as RabbitMQ or Kafka
• Familiarity with DataOps practices and data pipeline automation
• Familiarity with CI/CD pipelines and Kubernetes environments
• Background in FinTech or Trading domains

Responsibilities

• Communicate with technical and non-technical stakeholders to gather and clarify business requirements
• Review and analyze data and business logic to ensure consistency, accuracy, and reliability
• Design, implement, and maintain data pipelines for efficient and scalable data processing
• Integrate, maintain, and support data solutions and third-party data sources
• Research and evaluate third-party tools, services, and components for potential use
• Follow engineering best practices, including code reviews, testing, refactoring, and continuous delivery
• Design, optimize, and maintain data storage solutions

What we offer:

• Tax expenses coverage for private entrepreneurs in Ukraine
• Expert support and guidance for Ukrainian private entrepreneurs
• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays as per the company’s approved Public holiday list
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

We are seeking an experienced Data Engineer to develop and support reliable data infrastructure across our systems. In this role, you will create scalable data pipelines, maintain data accuracy and consistency, and connect external data sources. Your main goal will be to ensure smooth data movement and strengthen analytics capabilities throughout the organization. You will also help shape our data architecture, apply engineering best practices, and work closely with cross-functional teams to transform raw data into meaningful, actionable insights.

Requirements

• 5+ years of commercial experience in Data Engineering
• Experience gathering business requirements and communicating with stakeholders
• Strong knowledge of Data Warehouse (DWH) architecture and principles
• Hands-on experience designing data warehouses and building ETL pipelines
• Deep practical experience with Python
• Hands-on experience in one or more of the following areas: data processing using PySpark, Apache Spark, Pandas, or similar frameworks; data scraping; integration of third-party data sources and APIs
• Proficiency in SQL and relational databases such as PostgreSQL and MySQL
• Hands-on experience with column-oriented databases such ClickHouse, Snowflake, Cassandra, BigQuery (many tables with millions of records and above each)
• Solid understanding of software design patterns, algorithms, and data structures
• Intermediate English proficiency

Will be a plus

• Experience with messaging platforms such as RabbitMQ or Kafka
• Familiarity with DataOps practices and data pipeline automation
• Familiarity with CI/CD pipelines and Kubernetes environments
• Background in FinTech or Trading domains

Responsibilities

• Communicate with technical and non-technical stakeholders to gather and clarify business requirements
• Review and analyze data and business logic to ensure consistency, accuracy, and reliability
• Design, implement, and maintain data pipelines for efficient and scalable data processing
• Integrate, maintain, and support data solutions and third-party data sources
• Research and evaluate third-party tools, services, and components for potential use
• Follow engineering best practices, including code reviews, testing, refactoring, and continuous delivery
• Design, optimize, and maintain data storage solutions

What we offer:

• Tax expenses coverage for private entrepreneurs in Ukraine
• Expert support and guidance for Ukrainian private entrepreneurs
• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays as per the company’s approved Public holiday list
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)