Jobs Data Engineer

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.

Intetics Inc., a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is seeking a highly skilled and experienced Technical Coordinator / Solution Engineer to join our dynamic team on a full-time basis.

We are looking for a hands-on technical professional who will serve as the primary on-site technical expert, supporting deployment, configuration, automation, monitoring, and troubleshooting activities within a customer environment. This role is ideal for a specialist with strong infrastructure and operational experience who enjoys working independently while collaborating closely with remote engineering and product teams.

Responsibilities

• Install, configure, and support local server-side software components.
• Support local environment setup and configuration of server and viewer-related components.
• Manage and support secure data preparation, upload, and processing workflows.
• Develop and maintain basic orchestration and automation scripts.
• Monitor technical environments and proactively troubleshoot issues.
• Investigate problems related to setup, configuration, data uploads, processing steps, and environment stability.
• Maintain setup documentation, troubleshooting records, and operational procedures.
• Communicate effectively with remote product and vendor technical teams in English.
• Escalate product-specific, environment-related, and data-quality issues to appropriate technical stakeholders.
• Work predominantly on-site at the customer location in Kyiv, depending on project needs.
• Act as the main technical representative on-site while coordinating with remote support teams.

Requirements

Required Qualifications

• Strong experience working with Windows and/or Linux server environments.
• Proven experience installing, configuring, and supporting server-side or enterprise software solutions.
• Hands-on scripting experience with Bash, PowerShell, or Python.
• Experience creating and maintaining workflow automation and orchestration scripts.
• Strong background in deployment, configuration, monitoring, and troubleshooting of technical environments.
• Understanding of secure environments, access control, credential management, and sensitive data handling.
• Ability to read technical documentation and follow setup and design instructions.
• Experience collaborating with remote technical teams and participating in English-language meetings.
• Strong analytical and troubleshooting skills.
• Ability to work independently with minimal supervision.

Nice to Have

• DevOps or Infrastructure Engineering background.
• Experience with on-premises or hybrid infrastructure environments.
• Experience working with data processing workflows.
• Familiarity with visualization platforms, viewer/server environments, or similar technical systems.
• Basic knowledge of encryption and decryption workflows.
• Experience working with APIs and integrations.
• GIS, spatial data, or 3D visualization experience.
• Customer-facing technical support, consulting, or implementation experience.

Specific Requirements

• Must be based in Kyiv or willing to work primarily from Kyiv.
• Availability for regular on-site presence at the customer location.
• Comfortable working within a sensitive and secure customer environment.
• Openness to customer-side background or security screening if required.
• Ability to act as the primary hands-on technical specialist on-site.
• Strong sense of ownership, responsibility, and communication skills.
• Ability to properly identify, prioritize, and escalate issues when necessary.

What You Should Know

• This is not a pure software development role.
• This is not a project management role.
• The primary focus is hands-on technical execution, including setup, configuration, scripting, monitoring, troubleshooting, and operational support.
• Product-specific onboarding, documentation, API guidance, and training will be provided by the product/vendor team.

Benefits

• Paid Time Off — 36 paid days per year
• Training & Development opportunities

Partial medical compensation


 

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

About the team
We are a focused engineering team of 5 to 9 people at a multi-product tech SaaS company. We work across personalization, recommendation, fraud detection, and some GenAI, through real-time, near-real-time, and batch pipelines. The team is a blend of Python backend engineers, ML engineers, QA (manual and automation), a data engineer, and data scientists. Our shared mandate is to take experimentation from our data scientists and turn it into
robust, production-grade systems, either as standalone services or integrated into wider platforms.

The role
You will build the data pipelines that power our experimentation and production systems, the backbone behind fraud detection, recommendations, and personalization. You will work primarily with Kafka (sometimes Flink), Spark on Databricks, and Airflow, modelling and optimizing data for both experimentation and production serving, and partnering closely with data scientists and backend engineers on data contracts and reliability.

Responsibilities
• Build and operate batch and streaming data pipelines (structured & unstructured data)
• Work primarily with Kafka, Spark on Databricks, and Airflow
• Model and optimize data for both experimentation and production serving
• Partner with data scientists and backend engineers on data contracts and reliability
• Own pipeline quality: testing, monitoring, and performance
• Take part in the teams on-call rotation for the pipelines you build and operate

Requirements
• 3+ years of data engineering or software engineering for data

• Strong Python, including PySpark
• Kafka and stream processing
• Databricks experience
• Airflow or similar orchestration tools
• Strong SQL and data modeling, plus solid relational database skills
• Software engineering mindset
• Fluent spoken English, as you’ll work with an international team and all interviews will be conducted in English.

Nice to have
• Flink

• ML Feature stores
• MLOps and ML pipeline exposure
• Docker and Kubernetes
• SaaS or high-scale product domain experience

Why join us
• Work on high-scale, high-throughput systems
• Broad range of technologies, stacks, and problems
• Contribute to turning experimentation into production systems
• A strong engineering culture that values technical depth and ownership
• Flexibility through hybrid and remote working
• A distributed team across Dubai and Europe
 

You will get

• A position in a product development company with a dynamic environment and several concurrent projects;
• Continuous self-improvement and growth, including budget for certifications and courses;
• Competitive salary plus financial bonuses;
• Medical insurance coverage;
• Company prepaid AI agent;
• Wellbeing package: online-yoga classes, Yakaboo, BetterMe App: Health Coaching, BetterMe App: Mental Health;
• Corporate events and fun team-building activities.
• Remote-first culture
   

Interview Stages

1. HR Interview (30-45 minutes) — Initial conversation to discuss your experience, career goals, and cultural fit.
2. Technical Interview (1 hour) — In-depth technical interview covering relevant skills.
3. Final Interview (1,5 hour) — A comprehensive discussion with the team, focusing on role-specific competencies and alignment with company values.
4. Reference check & Job Offer

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

Job Description
Extensive software development experience, including experience working on complex backend or data infrastructure projects.
Strong hands-on experience with Scala.

Experience with Apache Iceberg or similar table formats and data lake technologies.

Hands-on experience developing backend services, data-processing engines, infrastructure components.
Programming experience in C /or C++ is highly desirable / preferred, the role is primarily focused on backend/system-level engineering rather than full-stack application development.
Deep understanding of distributed systems, data processing, storage engines, or data infrastructure.

Strong knowledge of data consistency, transactions, schema evolution, partitioning, compaction, and performance optimization.

Proven ability to troubleshoot complex production and customer issues.

Strong ownership mindset and the ability to operate effectively in areas with limited documentation or historical knowledge.

Excellent communication and collaboration skills.

Preferred Qualifications: 
Big Data Frameworks: Hands-on experience with distributed data-processing technologies such as Apache Spark, Flink, Trino, or Presto.
Cloud Object Storage: Experience working with cloud storage platforms, including Amazon S3, Google Cloud Storage (GCS), or Azure Data Lake Storage (ADLS).
Legacy Modernization: Proven track record of modernizing or gradually improving a mature codebase without disrupting existing customers.
Job Responsibilities
Architect & Build: Lead the architecture, design, and implementation of highly scalable, complex backend systems and robust data infrastructure using Scala.
Data Lake Engineering: Design and manage enterprise-grade data lake solutions leveraging Apache Iceberg or equivalent table formats, ensuring optimal data consistency, transactions, schema evolution, partitioning, and compaction.
Performance Optimization: Maximize system throughput, minimize latency, and optimize resource utilization across large-scale distributed data processing systems.
Production Excellence: Systematically diagnose, troubleshoot, and resolve complex, high-impact production and customer-facing infrastructure issues.
Codebase Modernization: Champion the gradual improvement and modernization of a mature, production-critical codebase without causing disruption to active customers or services.
Technical Leadership & Collaboration: Act as a technical authority, fostering strong communication and collaboration across engineering teams to align on technical objectives.
Navigate Ambiguity: Exercise a strong ownership mindset to operate effectively and deliver high-quality outcomes in areas with limited documentation or historical knowledge.
Department/Project Description
We are seeking an expert Scala Engineer to design, optimize, and scale our next-generation multi-cloud data lake house architecture across AWS and Azure. The ideal candidate is a backend/system engineer with strong experience building engines, infrastructure, or high-performance backend components. In this role, you will leverage Qlik Open Lakehouse (integrated with Qlik Talend Cloud) to establish a low-overhead, high-throughput data foundation. You will manage automated data ingestion, implement continuous table optimization, handle cross-cloud data mirroring, and ensure seamless multi-engine access without vendor lock-in.

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.

ORIL is seeking a highly experienced and autonomous Data Architect / Lead Data Engineer to lead a 2–3 month strategic project focusing on a comprehensive audit and redesign of our mission-critical, AI-driven real estate data platform.

The ideal candidate is a "rockstar" data expert with strong data architecture, data discovery, and data engineering expertise. You will take complete ownership of evaluating our current data layer, determining the target future state (Desired State), and defining a concrete execution roadmap for the engineering team.

About the Project

Our client is a US-based innovator providing a centralized data platform for multi-family real estate. The platform aggregates complex real estate datasets from various external integrations and sources to feed real-time BI analytics (Metabase), power automated operational insights, and drive an intelligent Text-to-SQL AI chatbot.

What are we looking for?

• 6+ years of experience as a Data Architect, Lead Data Engineer, or Senior Database Engineer;
• Proven track record of conducting end-to-end Data Discovery and Audits of complex, multi-source data architectures;
• Deep technical expertise in PostgreSQL and relational database design, including advanced query optimization, indexing, schema design, and data modeling;
• Hands-on experience with Data Quality, Data Governance, and Data Integrity frameworks (monitoring freshness, volume anomaly, schema drift, semantic metadata validation);
• Extensive background in evaluating and architecting ETL/ELT data pipelines and integrations (APIs, web scraping, CDC pipelines);
• Strong understanding of structuring metadata, schemas, and semantic layers for AI/LLM integrations (Text-to-SQL) and BI reporting tools (Metabase);
• Ability to translate complex business needs into high-level technical vision, concrete architecture blueprints, and resource estimates;
• English level: Fluent or Advanced (C1) with strong stakeholder-facing communication skills for direct interactions with US client leadership.

What will you do?

•  Evaluate the current state of all data collected across multiple integrations — how data is ingested, processed, stored, and presented across the platform.
• Analyze schema design, data freshness, bottleneck risks, and overall data health to identify system vulnerabilities and inaccuracies.
• Design a clean, scalable, and future-proof target data architecture optimized for fast querying, BI reporting (Metabase), and AI/LLM ingestion (Text-to-SQL).
• Build a detailed step-by-step execution plan (Roadmap) specifying required engineering steps, timelines, and resource allocations needed to reach the desired state.
• Present audit findings and strategic recommendations directly to the US client leadership and product team.

We Offer:

• Project-based contract engagement (2–3 months) with flexible Part-time or Full-time capacity;
• Potential for long-term cooperation upon successful project delivery;
• Competitive, market-leading compensation;
• Full accounting support;
• Fully remote work flexibility;
• Agile, transparent engineering culture.

Application Process:

1. Initial Screening Call with Recruiter.
2. Technical & Architecture Deep Dive with CTO.
3. Culture Index (CI) Test.

Stay Group is an innovative Marketing company with a mission to help businesses acquire their target customers and create top-tier solutions in the iGaming and entertainment industry. Currently, we are looking for our perfect DevOps Engineer to join our team and help us build scalable, reliable, and secure infrastructure.

Requirements:

• 3+ years of commercial experience as a DevOps Engineer.
• Strong hands-on experience with Kubernetes (GKE, EKS, and self-hosted clusters).
• Experience deploying and maintaining cloud infrastructure on AWS and/or Google Cloud Platform.
• Experience with CI/CD pipelines using GitLab CI/CD.
• Strong knowledge of Docker and containerized application deployment.
• Experience configuring and managing Cloudflare services, including WAF, SSL/TLS, DNS, and API automation.
• Experience with monitoring, logging, and observability tools:
  • Prometheus,
  • Grafana,
  • ELK Stack.
• Experience with database administration:
  • MySQL,
  • PostgreSQL,
  • MongoDB.
• Understanding of cloud cost optimization (FinOps).
• Experience with Infrastructure as Code and automation.
• Strong troubleshooting, analytical thinking, and ownership mindset.
• English level sufficient for reading technical documentation and communicating with international teams.

Will be a plus:

• Experience with Datadog (APM, RUM, Logs).
• Experience configuring Global Load Balancers in Google Cloud.
• Experience building scalable, high-load infrastructure.

Key Responsibilities:

• Design, build, and maintain highly available cloud infrastructure.
• Configure and manage Cloudflare services, including WAF, SSL/TLS, DNS, and API automation.
• Deploy, maintain, and optimize Kubernetes clusters across GKE, EKS, and self-hosted environments.
• Build and improve CI/CD pipelines using GitLab CI/CD.
• Automate Docker image builds and deployment processes.
• Implement and maintain monitoring, logging, alerting, and observability solutions using Prometheus, Grafana, and ELK.
• Optimize cloud infrastructure performance, scalability, and cost efficiency.
• Configure auto-scaling and load balancing for cloud environments.
• Administer and support production databases.
• Collaborate closely with Development, QA, Product, and Security teams to improve infrastructure reliability and delivery processes.

Benefits

Freedom to choose where to work: work from anywhere in the world with the freedom of remote options.

Flexibility: enjoy flexible working hours that let you balance your personal and professional life while staying focused on delivering great results.

Corporate English speaking club: improve your English proficiency with regular practice and expert guidance.

A Culture That Cares: thrive in a friendly environment where a strong corporate culture builds connections, drives innovation, and makes you feel truly valued.

Accelerate Your Growth: seize career advancement opportunities in a rapidly expanding marketing company. As the company scales, so will your professional journey.

Шукаємо у команду Senior DevOps / SRE Engineer (Remote).

Якщо ви маєте бажання працювати в невеликому колективі разом із сильною командою, радо розглянемо ваш відгук! Ми шукаємо не лише сильного DevOps Engineer, а й інженера із досвідом Site Reliability Engineering (SRE), який готовий брати на себе технічне лідерство, ділитися знаннями та допомагати розвиватися іншим членам команди. Націлені на довгострокову співпрацю з компенсацією, що базуватиметься на вашому досвіді роботи.
 

Завантаженість: повна зайнятість — 40 год/тиждень. Робота віддалена. Оплата погодинна.
 

Стек технологій:

- Scripting: Bash | Python | Makefile

- Containers and Orchestration: Kubernetes | Docker | GKE

- Infrastructure as Code (IaC): Terraform | Ansible

- CI/CD & Automation: GitHub Actions | CircleCI | TravisCI

- Cloud Providers & IaaS: AWS | GCP | Azure | Hetzner

- Databases: SQL | NoSQL (PostgreSQL, Redis…)

- Authorization and Security: OpenSSL

- Observability and Monitoring: Prometheus | Grafana | ELK Stack (Elasticsearch, Logstash, Kibana)

- Operating Systems: Linux
 

Site Reliability Engineering (SRE):
- SLI / SLO / SLA | Incident Response | Root Cause Analysis (RCA) | - High Availability | Capacity Planning | Disaster Recovery | 
- Performance Optimization | Reliability Engineering | 
 

Буде плюсом:

- Infrastructure Management: Developing and implementing infrastructure, configuration, and code branching strategies.

- Quality Assurance & Testing: Conducting integration, regression, UI/UX, API, load/stress, and other types of testing.

- Bug Tracking & Issue Management: Identifying, documenting, and tracking software defects and bugs.

- Performance & Optimization: Monitoring and analyzing quality metrics to identify areas for improvement.

- Automated Testing: Experience with automated testing frameworks such as Selenium, Appium, JMeter, etc.

Mentoring & Leadership:
- Досвід менторингу Junior/Middle інженерів, проведення code review, технічних обговорень, knowledge sharing та допомоги команді у професійному розвитку.
 

Вимоги до кандидата:

- Досвід роботи DevOps Engineer та практичний досвід Site Reliability Engineering (SRE)
- Досвід побудови та підтримки високодоступних production-систем із високими вимогами до reliability.

- Досвід роботи з Kubernetes у production-середовищі

- Розуміння принципів високої доступності, масштабування та відмовостійкості систем

- Досвід побудови та підтримки CI/CD процесів

- Англійська мова — Intermediate+

- Уміння слідувати code conventions

- Здатність навчатися

- Здатність наполегливо працювати

- Критичне мислення

- Витривалість

- Спроможність лаконічно та чітко пояснювати свою думку

- Бажання ділитися знаннями, проводити менторинг та допомагати розвиватися іншим членам команди

- Відданість нашим проєктам

- Прозорість
 

Що отримаєте:

- Віддалені умови роботи

- Удосконалення навичок планування та продуктивності

- Робота у вимогливому, але дружньому середовищі серед A-гравців

- Регулярний зворотний зв'язок і допомога в плануванні вашого навчального процесу та кар'єрного зростання

- Незалежність від місцевого ринку та долучення до глобальної економіки

- Підвищення рівня володіння англійською мовою

- Корпоративні навчання, технічні воркшопи та тренінги для постійного підвищення кваліфікації

We are looking for a Senior Data Engineer to join a strategic marketing technology (Martech) transformation initiative for a large enterprise organization. The project focuses on modernizing the client's marketing data platform, rebuilding data pipelines, and improving marketing attribution across multiple business brands.

As part of a dedicated data engineering team, you will help build a scalable, cloud-native data platform on Google Cloud Platform (GCP), enabling better visibility into marketing performance and supporting data-driven decision making. This is engagement through the end of 2026, working closely with the client's Data Engineering, Product, and Martech teams.

Responsibilities:

• Design, develop, and maintain scalable data pipelines on Google Cloud Platform (GCP).
• Build and optimize data models in BigQuery to support analytics and marketing reporting.
• Rebuild legacy marketing data pipelines following modern engineering best practices and enterprise standards.
• Contribute to the development of a canonical enterprise data model and semantic data layer.
• Support the implementation and adoption of DataHub as the enterprise data catalog.
• Develop automated CI/CD pipelines for data engineering workflows and enforce coding and data quality standards.
• Ensure proper data isolation between multiple business brands while enabling consolidated reporting where appropriate.
• Collaborate closely with Product Managers, Martech specialists, Analytics teams, and other data engineers to deliver business value.
• Optimize data processing performance, reliability, monitoring, and cost efficiency.

Required Qualifications:

• 5+ years of experience as a Data Engineer.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Advanced experience with BigQuery.
• Strong SQL and Python development skills.
• Experience designing and implementing modern ELT/ETL pipelines.
• Solid understanding of dimensional modeling, semantic modeling, and enterprise data architecture.
• Experience building production-grade CI/CD pipelines for data platforms.
• Familiarity with Git and modern software engineering best practices.
• Strong communication skills and ability to work within cross-functional teams.
• Upper-Intermediate or higher English.

Nice to Have:

• Experience with marketing technology (Martech) platforms and marketing attribution.
• Hands-on experience integrating: Google Ads, Google Analytics (GA4), Search Ads 360 (SA360), Campaign Manager 360, DV360
• Experience with DataHub or similar data catalog solutions.
• Experience implementing semantic layers for analytics platforms.
• Understanding of multi-brand or multi-tenant data architectures.
• Experience modernizing legacy data platforms and rebuilding pipelines from scratch.

ZentixSoft is looking for a Data Architect! 🚀

Format: Full-Time Outstaffing, long-term contract (12 months), 100% Remote.

💡 Why ZentixSoft?
 💸 Salary Review: A structured system of compensation reviews aligned with your engineering growth. 
⚖️ Work-Life Balance: Sustainable workflows with no senseless overtime, helping you keep your resource fully charged. 
🦾 Trust and Transparency: Zero micromanagement and no invasive time-trackers—we fully trust your professional autonomy. 
🎁 Gifts & Culture: Corporate gifts for holidays, birthdays, and a true team atmosphere where your voice always matters.
 

🧩 Role Overview & Responsibilities:

We are looking for a hands-on Data Architect to take full architecture ownership of a complex data platform. In this role, you will assess, stabilize, improve, and modernize the existing architecture, delivering rapid, actionable engineering recommendations.

• Architecture Ownership: Define tactical (immediate stabilization) and target (long-term modernization) architectures that engineering teams can directly execute.
• Platform Modernization: Assess complex, legacy data environments; identify performance/scalability bottlenecks; and deliver clear, implementation-ready architectural outputs.
• Unstructured & Structured Data: Architect data pipelines and modeling frameworks capable of handling unstructured data (documents, PDFs, free text, scanned content, images) and structured/file-based datasets.
• Tech-Neutral Evaluation: Evaluate enterprise technologies based on scale, cost, workload, and business requirements rather than preference (determining when Databricks, Synapse, ADF, or Azure SQL are truly justified).
• Data Modeling & Pipelines: Design robust ETL/ELT frameworks, dimensional schemas, star schemas, and incremental processing/refresh strategies.
• Documentation & Governance: Create detailed architecture diagrams, technical decision logs, and data governance standards.
   

🎓 Our Perfect Match (Requirements):

• Proven Architecture Ownership: 4+ years of recent, demonstrated experience as a Data Architect owning overall Azure Data Platform architectures (candidate must be an Architect, not solely a Data Engineer).
• Azure Data Platform Expertise: Extensive hands-on background with Azure Data Services, including Azure SQL / SQL Server, Azure Data Lake, Azure Data Factory (ADF), Azure Synapse, and Databricks.
• Azure Cosmos DB (MUST-HAVE): Hands-on experience working with Azure Cosmos DB and file-based data stores within enterprise environments.
• Data Engineering Core: Strong mastery of Python, SQL, ETL/ELT, and Lakehouse architectures.
• Unstructured Data Experience: Proven track record handling unstructured data sources (documents, PDFs, text, scanned files).
• Fast Delivery & Problem Solving: Ability to onboard quickly into complex environments, navigate incomplete documentation, and deliver meaningful architectural guidance rapidly.
• Communication & English: Fluent English (C1) with exceptional technical writing and stakeholder communication skills.
   

📍 Project Details:

⏳ Duration: 12 Months (Full-time, 100% Remote).

🕒 Time Zone: CET / CEST.
 

We look forward to receiving your CV (please ensure your CV details your project contexts, architecture ownership, and hands-on Cosmos DB/Azure experience).

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

Must Have

• 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.