Jobs Data Engineer

Are you passionate about safeguarding countries, societies, and businesses from hybrid threats?

Look no further! Our rapidly scaling company is committed to this crucial mission, collaborating with some of the world’s most influential organizations, including NATO and the EU.

If you’re ready to join a high-impact team shaping the future of intelligence and security, we invite you to become part of our journey. Join us and help defend the digital world with purpose and innovation!

Role Overview

We’re looking for a Senior Data Engineer to own the data foundation behind our solutions: how data is processed, stored, linked and served. Every month we process billions of data points of different types (text, images, social media, geospatial and time-series data). Once data is collected by our data fetching services, your job is to make sure it’s processed reliably, connected across sources and storage systems, and fast to query, so that analysts and AI agents get what they need in seconds.

You’ll work closely with our AI Engineers, who own the entity model and the knowledge layer on top of the data, and you’ll give them a fast and solid data foundation to build on. 

This is a hands-on senior role with real ownership: you’ll choose the tools, run experiments and own the results end to end, from pipelines and infrastructure to the features our users see.

Key Responsibilities

Multimodal data and storage

• Build and own the pipelines that take data from our collection services, then process it, enrich it and load it into our data storage at scale.
• Decide how each type of data (text, images, geospatial, time-series) is stored and indexed across relational, columnar, search and other storage, so that it can be queried efficiently together.
• Link data across storage systems so that everything related to the same object can be retrieved quickly and consistently.
• Provide fast, reliable data access for AI engineers, analysts and product services.
• Work with product, data fetching and AI teams to deliver data-driven features to end users, such as new data sources in the product, faster search, alerts and reports.

Core data platform

• Migrate production workloads from PostgreSQL to StarRocks and redesign storage for billion-row scale.
• Improve the performance, reliability and cost of data processing.
• Own the deployment and infrastructure of the data platform.

Requirements

• 5+ years in data engineering (or backend engineering with a strong data focus), working on production systems that process large data volumes.
• Hands-on experience with several types of storage (e.g. relational, columnar/OLAP, search engines, document stores, object storage) and the judgment to pick the right one for each task.
• Experience working with different data modalities. Text is a must; images and/or geospatial data are a strong plus.
• Experience building large-scale pipelines that are resumable, idempotent and memory-safe.
• A track record of building data platforms and custom solutions yourself, not only working within managed platforms (e.g. Databricks).
• Experience delivering data that powers user-facing product features.
• Ability to read query plans and restructure physical data layout for performance.
• Production experience with Python backend services and APIs.
• Comfort owning infrastructure (Docker, Kubernetes, CI/CD, infrastructure as code).
• Active use of AI tools in your daily work.
• Upper-intermediate level of English or higher.

Nice to Have

• Understanding of knowledge graphs, ontologies, entity extraction, linking or resolution.
• Graph databases or graph modelling on relational or columnar storage.
• GIS tooling (e.g. PostGIS).
• StarRocks, ClickHouse or other MPP engines; Elasticsearch or OpenSearch at scale.
• Kafka or RabbitMQ, Airflow, dbt.
• Long-running dual-write migrations, PostgreSQL internals, observability.
• A background in OSINT, threat intelligence or defence.

We Offer

• Opportunity to contribute to a mission-driven startup and support prestigious clients, including governments worldwide, enterprise clients, and leading NGOs in tackling global security challenges.
• Autonomy and freedom to drive experiments and bring your own ideas to life.
• Flexibility of fully remote work.
• Flexible, unlimited time-off policy.

Gensoft is hiring a hands-on Senior Data Engineer to join an AI-powered search and matching platform. Your focus will be the data pipelines that feed production search: making ingestion fast, reliable and easy to operate, and keeping the data that reaches the matching layer clean and consistent.

We are looking for a strong production data engineer first: deep ETL/ELT, SQL/PostgreSQL, data quality, recovery and workflow orchestration experience matters most. Go is used in the existing data migration layer, so you need to be comfortable reading and following Go code, but this is not an advanced backend Go role.

Engagement

• Full-time contract, 6 months to start, with a strong chance of extension
• Fully remote
• Monday to Friday, 8 hours a day, with overlap with the team's core working hours

• Start: as soon as possible

   

Tech stack

PostgreSQL with pgvector, embedding models (VoyageAI) served through Amazon Bedrock, DAG-based workflow orchestration, and an existing Go layer that runs data migrations.

What you'll do

• Take over the current ingestion flow and document how it works: sources, transformations, weak points and failure scenarios
• Automate ingestion, validation, deduplication and incremental updates, and turn onboarding of new data sources into a repeatable process
• Build and run ETL/ELT workflows as DAGs: manage dependencies between tasks, configure retries, handle failed runs and run backfills
• Build safe recovery mechanisms (retries, checkpoints, replay and backfill, quarantine) so that no data is lost or duplicated
• Track record counts, metadata, source identifiers and data lineage; catch schema changes and run quality checks
• Own the pipelines that generate embeddings and keep the vector index in sync with the source data (re-embedding, re-indexing, consistency checks)
• Support the interfaces of the matching service and take part in improving match quality
• Investigate data-flow issues in the existing Go migration layer together with platform engineers
• Set up monitoring and alerting together with DevOps (freshness, failures, throughput, latency, cost)
• Keep tests, deployment practices and runbooks in shape so the system can be run and recovered without you
• Apply the agreed rules on data consent, access, retention and deletion, and flag any open questions early

• Work closely with Product, Engineering, DevOps and ML specialists, keep the team updated on progress

   

What we expect

• Proven ownership of production data pipelines, including debugging and recovering failures
• Strong hands-on ETL/ELT experience in production
• Hands-on experience with DAG-based workflow orchestration (Apache Airflow, Dagster or equivalent), including dependencies, retries, failed runs and backfills
• Strong SQL and PostgreSQL data modelling skills
• Working knowledge of Go: enough to read existing code, follow how data moves through it and investigate issues (advanced backend Go experience is not required)
• Solid understanding of APIs, batch and async processing, schema evolution, metadata and lineage
• Experience with idempotent processing, reconciliation and safe reruns
• Hands-on work with vector search and embedding pipelines in production (search, matching or ML products): generating and storing embeddings, indexing and keeping them consistent with the source data
• Ability to trace quality problems across inputs, embeddings, indexes and downstream results
• Cloud experience, automated testing, code review and disciplined deployments

• Monitoring, alerting, runbooks, privacy-aware data modelling and access control

   

Nice to have

• Direct experience with pgvector and Amazon Bedrock
• Experience integrating and evaluating embedding models (e.g. VoyageAI)
• Python for data processing
• Entity resolution and search relevance

• Background in HR tech or recruitment data

   

When applying, please make sure your CV clearly shows

• ETL/ELT and workflow orchestration projects (which tool, what the DAGs did, how you handled failures and backfills)
• Production embedding and vector search work (which models, which vector store, scale, your personal role)
• Examples of ingestion recovery and data quality work, with your individual contribution and impact

• A link to code repositories, if available

   

Hiring process

CV review, technical interview, pair programming session, final interview.

Phoenix Games is a technology company building next-generation iGaming and Web3 products for a global audience. We leverage AI, cloud technologies, blockchain, and advanced analytics to create intelligent, data-driven gaming experiences.

As we continue to scale, we're looking for Data Engineer / Data Scientist to help us build modern data infrastructure, develop machine learning solutions, and turn massive volumes of data into actionable insights.

What You'll Do

• Design, build, and maintain scalable data pipelines and ETL/ELT processes.
• Develop and optimize data warehouses and data lake architectures.
• Collect, clean, and transform large datasets from multiple sources.
• Build predictive models, recommendation systems, and analytical solutions.
• Apply machine learning techniques to improve player engagement, fraud detection, personalization, and business intelligence.
• Design dashboards and reporting solutions for business stakeholders.
• Collaborate with Product Managers, Engineers, and BI teams to define data-driven strategies.
• Optimize data processing performance and ensure data quality.
• Implement monitoring, validation, and governance for data pipelines.
• Research and evaluate new AI and machine learning technologies.

Tech Stack

• Python
• SQL
• PostgreSQL
• Snowflake / BigQuery / Redshift
• Apache Airflow
• Spark
• Pandas
• NumPy
• Scikit-learn
• TensorFlow / PyTorch
• MLflow
• Docker
• Kubernetes
• AWS / Azure / GCP
• Git
• Power BI / Looker / Tableau

Requirements

• 2+ years of commercial experience in Data Engineering, Data Science, or Machine Learning.
• Strong Python programming skills.
• Advanced SQL knowledge.
• Experience building scalable data pipelines.
• Experience with cloud data platforms.
• Strong understanding of data modeling and database design.
• Experience with machine learning algorithms and model evaluation.
• Experience working with large datasets.
• Familiarity with Docker and cloud environments.
• Strong analytical and problem-solving skills.
• Upper-Intermediate English or higher.

Nice to Have

• Experience with LLMs, Generative AI, or AI agents.
• Knowledge of real-time data streaming (Kafka, RabbitMQ).
• Experience with feature engineering and MLOps.
• Experience with fraud detection or recommendation systems.
• Familiarity with blockchain analytics or Web3 ecosystems.

What We Offer

• Fully remote work from anywhere in Europe.
• Long-term B2B cooperation.
• Competitive salary paid in EUR or USD.
• Annual salary reviews.
• Performance-based bonus program.
• Annual company bonus.
• 25 paid vacation days.
• Paid sick leave.
• Flexible working hours.
• Home office budget.
• Laptop or equipment allowance.
• Learning & development budget for courses, certifications, conferences, and books.
• English language lessons.
• Private health insurance.
• Mental health support program.
• Sports and wellness compensation.
• Team-building activities and international company retreats.
• Opportunity to work on AI, Web3, blockchain, and large-scale data platforms.
• Clear career growth opportunities within a rapidly expanding international product company.

Why Join Phoenix Games?

At Phoenix Games, data is at the core of every product decision. You'll work with modern technologies, solve complex data challenges, and help build intelligent systems that power the next generation of online gaming.

If you're passionate about data engineering, machine learning, and creating impactful AI-driven solutions, we'd love to hear from you.

Job Title:

Senior Data Engineer

Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.

About the Product:

The product brings together data about employees, devices, applications, and IT operations to help organizations understand and improve the digital workplace experience. It combines information from multiple systems into a consolidated view, enabling IT teams to identify issues, analyze trends, and take action.

Its underlying platform ingests continuously changing data, normalizes it, and maintains relationships between employees, departments, devices, and organizational structures. Role-based access controls govern how that information is used. The engineering challenge is to make high-volume data flows reliable and efficient while providing low-latency access to current information.

About the Role:

As a Senior Data Engineer, you will build the streaming pipelines and distributed services that support this platform. This is a hands-on role centered on Java, Kafka, and Apache Flink. You will work from product requirements and architectural direction while owning complex implementation decisions, identifying risks early, and delivering production-ready components with minimal supervision.

You will join a small team developing the product and work with architecture, product, and DevOps colleagues. Early work includes understanding existing schemas and data sources, planning how data enters the platform, defining transformations and aggregations, and implementing, testing, and deploying those flows. The role involves substantial work on throughput, latency, persistence, reliability, and the operational behavior of systems processing data at scale.

Key Responsibilities:

• Build and maintain production Java services and high-throughput data-processing components.
• Design, implement, and optimize Kafka and Apache Flink pipelines that combine data from multiple sources.
• Plan and implement data flows, including ingestion, normalization, calculation, aggregation, and persistence.
• Develop the data model and services for employees, departments, devices, organizational relationships, and role-based access control.
• Build service interfaces and data contracts using technologies such as gRPC and Protobuf.
• Design persistence and data-access patterns across relational, analytical, and in-memory systems, including PostgreSQL, ClickHouse, and Redis where appropriate.
• Optimize schemas, queries, indexes, partitioning, and ingestion for large datasets.
• Diagnose bottlenecks and production incidents across streaming pipelines, services, and databases.
• Improve scalability, reliability, observability, throughput, latency, and operational cost.
• Use AI-assisted development tools while independently reviewing their output for correctness, security, and maintainability.

Required Competence and Skills:

• Typically 10+ years of experience building and maintaining production systems, including substantial hands-on work with distributed, high-throughput data platforms.
• Typically 5+ years of professional Java experience, with a strong grasp of concurrency, memory management, and JVM performance tuning.
• Typically 3+ years of production Kafka experience, including partitioning, consumer groups, schemas, delivery guarantees, fault tolerance, and throughput tuning.
• Typically 2+ years of production Apache Flink experience, including stateful processing, windowing, checkpointing, backpressure, fault recovery, and pipeline optimization.
• Experience building and operating systems that process millions of events per minute, with direct ownership of scaling or production performance decisions.
• Strong data-modeling and database optimization skills, including work with time-series data, high-volume ingestion, analytical workloads, and PostgreSQL or another relational database.
• Experience with Protobuf, schema evolution, and compatibility in distributed systems; familiarity with gRPC and service-to-service communication.
• Hands-on experience monitoring, troubleshooting, and improving distributed production workloads, including incident resolution and resource management.
• Practical use of AI-assisted or agentic coding tools, paired with critical review of generated code before production use.
• Ability to own complex components independently, assess technical tradeoffs, and deliver maintainable solutions.

Nice to Have:

• Experience with ClickHouse or a comparable large-scale analytical database.
• Practical familiarity with CI/CD, Docker, Kubernetes, and production observability.
• Experience with TypeScript and React, including backend services or user-facing applications.
• Experience unifying data from multiple sources in Kafka-based pipelines.
• Experience with real-time analytics, anomaly detection, or complex event processing.
• Experience integrating AI or LLM-based capabilities, agentic workflows, or tool-based automation into production systems.

Why Us:

We provide 20 days of vacation leave per calendar year (plus official national holidays of a country you are based in).

We provide full accounting and legal support in all countries we operate.

We utilize a fully remote work model with a powerful workstation and co-working space in case you need it.

🌊 Data Engineer: Build the Lakehouse Behind Millions of Players

Adaptiq × a major online gaming operator

The challenge:

Our client, a major online gaming operator, is building a next-generation account management platform as a greenfield project. It runs in a highly regulated, high-volume environment where availability, sub-second performance, data integrity, and full auditability are non-negotiable, including during extreme traffic spikes.

Every transaction becomes data, and that data has to be right. How do you ingest it without losing or duplicating a single event? How do you keep it fresh and reconciled with source systems? How do you store years of history so it’s cheap to keep, fast to query, and GDPR-compliant? These are the questions you’ll answer.

Your role:

We’re looking for a hands-on Data Engineer to build scalable, reliable data infrastructure and production-grade pipelines, with a strong focus on data quality, integrity, and performance.

You’ll own the design, implementation, and operation of the data lake and lakehouse architecture, including key technical decisions. You’ll define how data is stored, partitioned, processed, and monitored, work closely with Data Engineering, DevOps, Data Science, and other stakeholders, and help shape the platform’s long-term scalability and the data migration.

What you’ll do:

• Build and maintain a multi-layer lakehouse (bronze / silver / gold) on Iceberg or Delta, including schema evolution
• Design CDC and streaming ingestion with Kafka, Debezium, or similar, handling late and duplicate events
• Optimize data layout in cloud object storage: partitioning, compaction, file sizing, query performance
• Define retention, archival, immutability, and GDPR-style deletion processes
• Ensure data freshness, quality, and consistency; detect drift and reconcile with source systems
• Implement governance: metadata, lineage, access controls, PII protection, encryption, audit trails
• Monitor pipelines and drive resolution of data anomalies

• Collaborate with Analytics, Finance, and Compliance on requirements, migration, and architecture

   

What you bring:

• 5+ years in data engineering with large-scale data lakes or lakehouse environments
• Production experience with cloud object storage (AWS S3 or equivalent) and an analytics platform such as Snowflake, Databricks, or BigQuery
• Hands-on expertise with Iceberg, Delta, or Hudi, plus solid knowledge of partitioning, file layout, and query optimization
• Experience building CDC and streaming pipelines (Kafka, Debezium, or similar)
• Strong SQL, data modeling, and understanding of relational and transactional data
• Experience with data quality, monitoring, and governance: lineage, access control, PII handling, retention
• Python and workflow orchestration (Airflow or Dagster)

• Ability to own technical decisions and clearly explain trade-offs

   

Bonus points:

✦ Financial, transactional, or ledger data
✦ Regulated or audit-heavy environments: fintech, gaming, banking, payments
✦ Large-scale data migration projects

If you want your pipelines to carry real money, real scale, and real responsibility, let’s talk.

Join a high-impact cybersecurity project focused on protecting applications from automated attacks and malicious activity. We are looking for a Senior Data Engineer who thrives in autonomous environments, enjoys solving complex data challenges, and is ready to drive modernization initiatives across scalable data systems.

You will become a core member of a small, highly proactive engineering team working on a large-scale cybersecurity product. The role requires deep involvement in understanding existing systems, including legacy code and interconnected services, identifying inefficiencies and technical gaps, and driving meaningful improvements across the platform. You will collaborate closely with product engineering teams, influence technical decisions, and contribute to both immediate optimizations and long-term platform evolution.

As part of Sigma Software, you will collaborate with experienced international teams and have the opportunity to shape scalable data infrastructure used in cybersecurity products operating at scale. We value ownership, strategic thinking, and technical excellence, offering an environment where engineers can influence architectural decisions, drive meaningful improvements, and continue growing professionally.

Customer

Our Customer is a global cybersecurity company delivering advanced digital protection solutions for businesses, organizations, and communities worldwide. Operating across multiple cybersecurity domains, the company focuses on identifying vulnerabilities, preventing threats, and strengthening digital resilience through scalable and reliable security products.

Project

The project focuses on developing and improving a cybersecurity solution designed to protect applications from automated attacks and malicious traffic. The team works on scalable, high-performance data infrastructure powering security analytics, monitoring, and detection capabilities. The environment combines modernization of legacy systems with the development of reliable and scalable data workflows, requiring strong ownership and close collaboration with cross-functional engineering teams.

Key Technologies: Snowflake, DBT, SQL, Python, Elastic Stack, Airflow

Requirements

• 5+ years of commercial experience in Data Engineering
• Expert-level Python skills
• Expert-level SQL skills with practical experience optimizing complex queries for performance and scalability
• Strong hands-on experience with DBT for data transformation and modeling
• Solid understanding of database concepts, including DDL, DML, indexing, normalization, and advanced SQL techniques
• Hands-on experience working with Snowflake
• Experience with Elastic Stack technologies
• Proven experience designing, building, and maintaining large-scale data pipelines
• Strong analytical thinking and problem-solving skills
• Ability to work independently in a high-autonomy engineering environment
• Experience driving technical initiatives from idea through implementation
• Strong communication and collaboration skills
• Upper-Intermediate English level or higher

WILL BE A PLUS

• Experience with Airflow for workflow orchestration

Responsibilities

• Maintain and enhance existing data processes with a focus on reliability, scalability, and performance optimization
• Analyze and deeply understand existing data models, workflows, tables, and processing jobs
• Identify technical gaps, bottlenecks, inefficiencies, and opportunities for architectural improvements
• Design, optimize, and support large-scale data pipelines and transformation processes
• Drive modernization initiatives across the data platform and contribute to long-term architectural evolution
• Optimize complex SQL queries and improve data processing latency and efficiency
• Collaborate with product engineering and cross-functional teams on technical solutions and implementation strategies
• Contribute to both short-term delivery goals and long-term platform development initiatives
• Ensure adherence to best practices in data engineering, system reliability, and performance optimization
• Participate in technical discussions, solution design, and continuous improvement initiatives

Project description

Our client is an energy solutions provider. Our AI-enabled platform supports operations, tracking and decision-making to optimize large-scale renewable and traditional energy portfolios. We're delivering a key client MVP in February 2027, and the project continues into 2028. Our stack: Ruby on Rails, Node.js/TypeScript and Python services, PostgreSQL, Snowflake and AWS. We're looking for an autonomous data engineer to strengthen our data foundation through good modeling, clean schemas, reliability and automation.

Responsibilities

Work closely with product teams to help define and execute technical criteria

Architect and maintain robust data ingestion pipelines that feed both our application and analytics

Design well-modeled, layered tables with clean schemas that turn raw data into analytics-ready data

Help onboard client data from a variety of sources, such as Databricks via Delta Sharing, and integrate it with our existing services and workflows

Make pipelines reliable and automated: data quality tests, monitoring, safe reprocessing and schema change handling

Build a documented, well-tested data foundation that serves the app, analytics and future AI use cases

Skills

Must have

Mid-to-senior data engineer who has owned the design of production data pipelines end to end, working as an autonomous IC

Designing and building production ingestion pipelines from APIs, files and shared-data sources, using a workflow orchestrator (any tool, e.g. Airflow, Dagster or Step Functions)

Production experience with a cloud data warehouse (Snowflake preferred)

Strong SQL, plus production experience in Python (preferred) or Node.js/TypeScript

AWS or another enterprise cloud platform

AI-native development: daily use of agentic AI coding tools, and experience scaling shared AI workflows across a team and into the development lifecycle, including CI/CD

Strong English and communication skills

Nice to have

dbt

Preparing data for AI/LLM use (e.g. semantic layers, metadata, retrieval-ready datasets)

Infrastructure as code (e.g. Terraform)

Exposure to energy, utilities or sustainability data

Languages

English: B2 Upper Intermediate

Binariks is looking for a Senior Data Engineer to connect an emerging AI platform to large-scale healthcare and pharma data. The role will build the pipelines, curated facts and semantic models that feed both AI agents and a knowledge graph.

The project is an emerging AI decision-intelligence platform for pharmaceutical commercial teams, developed for a US healthcare data and analytics company. It turns daily specialty-pharmacy, hub, payer and clinical data into AI-generated recommendations that show why they were made and who acts on them, then measures whether they worked. The role will join a senior Binariks team working alongside the client's product and engineering teams on AWS.

What we are looking for:

• 5+ years in data engineering with a solid track record in client-facing environments
• Strong practical experience architecting and running production environments on Databricks (Delta Lake, Unity Catalog, Workflows)
• Proficiency in Python (PySpark) and advanced SQL for complex data transformations and high-performance querying (PostgreSQL / Amazon RDS)
• Proven hands-on track record building robust batch and streaming architectures on AWS (experience with GCP is also considered)
• Experience defining business metrics, data abstraction layers, or knowledge graph feeds
• Deep understanding of data observability, lineage, metadata management, and data quality tooling
• Familiarity with entity resolution algorithms, record deduplication, and master data management practices
• Strong communication and collaboration skills, with the ability to work effectively within a cross-functional team
• Working proficiency in English (B2)

Your Responsibilities:

• Design, build, and maintain Databricks Lakehouse components using Delta Lake, Unity Catalog, and orchestrated Databricks Workflows
• Develop and optimize both batch and event-driven data pipelines across the AWS ecosystem (S3, AWS Glue, Athena, AWS Lambda, and streaming services)
• Perform robust SQL development and data modelling (including PostgreSQL / Amazon RDS), along with semantic lakehouse modelling for business entities and metrics powering knowledge graphs
• Implement and oversee automated data quality frameworks, data lineage tracking, and governance standards across the platform
• Lead master data management (MDM) initiatives and entity resolution pipelines to consolidate core entities (such as patients, healthcare professionals/HCPs, and healthcare accounts)

Will Be a Plus:

• Familiarity with healthcare standards and terminologies (FHIR, HL7, OMOP, RxNorm, SNOMED)
• Experience in Pharma, Life Sciences, or HealthTech domains

Binariks is looking for a Data & AI Engineer (Semantic Layer) to design the knowledge that AI agents reason over. This covers how drugs, payers, coverage rules, prescribers and patient journeys connect, and the shared vocabulary every agent relies on.

The project is an emerging AI decision-intelligence platform for pharmaceutical commercial teams, developed for a US healthcare data and analytics company. It turns daily specialty-pharmacy, hub, payer and clinical data into AI-generated recommendations that show why they were made and who acts on them, then measures whether they worked. The role will join a senior Binariks team working alongside the client's product and engineering teams on AWS.

What we are looking for:

• 5+ years in data engineering, analytics engineering, or solutions engineering, with a solid track record in client-facing environments
• Experience integrating data/semantic context with LLM applications (RAG, GraphRAG, metadata-driven retrieval, or structured outputs) using Python
• Deep expertise in conceptual, logical, and physical data modelling - spanning ER, dimensional, and canonical models, with the ability to map complex business concepts directly to clean data structures
• Hands-on experience with modern data platforms: strong SQL, hands-on experience with AWS or GCP (AWS preferred), and working knowledge of Databricks (Delta Lake, Unity Catalog)
• Experience with data catalog and metadata management with tools such as DataHub, Collibra, Alation, Unity Catalog, Dataplex or Glue, including business glossaries and lineage
• Strong interpersonal skills to facilitate discovery workshops, translate domain jargon into robust technical designs, and articulate trade-offs clearly to non-technical stakeholders.
• Working proficiency in English (B2+)
• High comfort navigating ambiguity, rapid context-switching, and operating embedded directly within client organizations

Your Responsibilities:
 

• Design semantic models, business glossaries, and domain taxonomies to capture complex business logic, utilizing ontology standards (such as RDF/OWL or graph models) where applicable
• Implement semantic models (business entities, metric definitions) directly within Databricks Unity Catalog
• Design the foundational semantic contracts for AI agents (shared vocabulary, entity IDs, permission models, context scoping)
• Integrate knowledge graphs with LLM applications (implementing GraphRAG and semantic context retrieval)
• Build, automate, and refresh graph structures from underlying relational and lakehouse data sources
• Manage graph quality through validation, version control, lineage tracking, provenance, and data governance
• Facilitate discovery sessions with domain SMEs to capture business terms, logic, rules, and relationship mappings

Will Be a Plus:

• Practical background with OWL, RDF, SKOS, SHACL, or Protégé
• Experience building and querying graph databases (Neo4j, AWS Neptune, Stardog, GraphDB) via Cypher or SPARQL
• Hands-on work with GraphRAG
• Familiarity with regulated domain standards such as FHIR (Healthcare) or ACORD (Insurance)

Join the Dataforest Team!

We are actively expanding our team and looking for strong new talent to join our ranks. If you are a Data Engineer seeking a friendly team, a healthy working environment, and a flexible schedule — you have found the right place to send your CV!

You'll be a great fit if you have:

- 2+ years of experience with Python;
- 2+ years of experience as a Data Engineer;
- Experience with Pandas;
- Experience with SQL DB / NoSQL (Redis, Mongo, Elasticsearch) / BigQuery;
- Familiarity with Amazon Web Services;
- Experience with Kafka;
- Knowledge of data algorithms and data structures is a MUST;
- Working with high volume tables 10m+.

What you'll do:

- Create ETL pipelines and data management solutions (API, Integration logic);
- Different data processing algorithms;
- Involvement in creation of forecasting, recommendation, and classification models.

Would be a plus:

- Experience with Spark (pyspark);
- Experience with Airflow;
- Experience in statistics;
- Knowledge of DS and Machine learning algorithms.

We ensure your growth with:

- Great networking opportunities with international clients, challenging tasks;
- Building interesting projects from scratch using new technologies;
- Personal and professional development opportunities;
- Competitive salary fixed in USD;
- Paid vacation and sick leaves;
- Flexible work schedule;
- Friendly working environment with minimal hierarchy;
- Team building activities, corporate events.

Dataforest is looking for a growth-oriented Data Engineer to join our friendly team. As a Data Engineer, you will solve engaging problems using cutting-edge technologies for data collection, processing, analysis, and monitoring.

If you aren't afraid of challenges, this position is just for you!

You'll be a great fit if you have:

- 1+ years of experience as a Data Engineer;
- Strong experience with Python;
- Proven experience with Databricks and Datafactory;
- Experience with AWS/Azure;
- Experience building ETL / ELT pipelines;
- Proficiency in SQL.

What you'll do:

- Developing ETL/ELT pipelines and data management solutions;
- Implementing data processing algorithms;
- Writing and optimizing SQL queries for data extraction and analysis;
- Analyzing data and applying processing algorithms to solve business problems.

We ensure your growth with:

- Working with a high-skilled engineering team on engaging and complex projects;
- Hands-on experience with the latest technologies;
- Direct communication with international clients and challenging tasks;
- Opportunities for personal and professional growth;
- Competitive salary pegged to USD;
- Paid vacation and sick leave;
- Flexible working hours;
- Friendly working atmosphere with zero bureaucracy;
- Plenty of team traditions — corporate parties, team buildings, themed events, and much more!

Шукаємо Senior / Lead Software Engineer (Data), який допоможе будувати й масштабувати production-grade data systems: пайплайни, сервіси та storage-рішення для збору, обробки, зберігання й доставки даних. Це роль для інженера з сильним software engineering mindset, який вміє працювати не лише на рівні реалізації, а й на рівні архітектури, data modeling, reliability, performance та technical leadership. Очікуємо, що людина зможе брати end-to-end ownership за критичні data workflows і впливати на стандарти команди.

Обовʼязки:

• Проєктувати, розробляти та підтримувати batch/streaming data pipelines і data services.
• Приймати технічні рішення щодо ingestion, orchestration, storage, трансформацій і delivery.
• Будувати ETL/ELT-рішення з використанням Python, Airflow, Cron.
• Інтегрувати дані з API, логів, NoSQL, IoT/embedded-джерел та внутрішніх сервісів.
• Проєктувати схеми зберігання, моделі даних і структури баз даних.
• Оптимізувати рішення з погляду performance, scalability, cost та maintainability.
• Забезпечувати data quality, observability, reliability і стабільність production-процесів.
• Планувати та реалізовувати міграції даних між платформами й форматами.
• Документувати схеми, трансформації, data lineage та ключові технічні рішення.
• Брати участь у code review, design discussions, incident resolution.
• Менторити інших інженерів і розвивати engineering standards у data-напрямку.
   

Вимоги:

• Значний досвід як Senior / Lead Software Engineer, Data Engineer або Backend Engineer у data-intensive середовищі.
• Сильний Python і досвід побудови production-ready рішень.
• Досвід роботи з Apache Airflow.
• Досвід роботи з MongoDB або іншими NoSQL-рішеннями.
• Сильне розуміння проєктування баз даних і data models: schema design, indexing, normalization/denormalization.
• Досвід роботи з великими обсягами даних, high-throughput або streaming workloads.
• Досвід із Azure Blob Storage або аналогічними object storage.
• Впевнене знання SQL та оптимізації запитів.
• Глибоке розуміння ETL/ELT, incremental processing, datalake/warehouse principles.
• Розуміння testing, observability, fault tolerance, idempotency, recovery для data systems.
• Досвід із Git, CI/CD, production deployment.
• Досвід технічного лідерства, mentoring, design review і роботи зі стейкхолдерами.
   

Буде плюсом:

• Snowflake, Elasticsearch
• Kafka або інші streaming-платформи
• Grafana / Prometheus
• Ansible або інші IaC / automation tools
• Power BI
• Досвід із cloud DWH, governance, lineage, internal data tools
  
  Надаємо бронювання*
  Офіс з повноцінним укриттям*

Join an exciting journey to build and scale a global Media Data Foundation for a leading global organization based in Europe. This platform brings together paid media, digital analytics and marketing performance data from dozens of advertising and measurement platforms. It unlocks reliable media reporting, marketing ROI measurement and data-driven investment decisions across markets.

As a Senior Data Engineer, you will play a pivotal role in shaping and implementing the platform's architecture. The focus is on hands-on technical execution and collaboration with cross-functional teams.

Your work will turn raw media and marketing data into trusted, harmonised data products used by marketing, media and analytics teams around the world. You will use modern tools to solve complex challenges, working alongside skilled architects, engineers and business stakeholders.

Key Responsibilities:

• Hands-On Development: Build, maintain and optimize data pipelines for ingestion, transformation and delivery of media data (Meta, Google Ads, DV360, TikTok, The Trade Desk, GA4, CM360 and others).
• Create and implement scalable solutions to handle many data sources, frequent API and schema changes, and high volumes of data.
• Data Modeling & Warehousing: Design and maintain efficient, harmonised data models for cross-platform media reporting in Snowflake.
• Lead the move of transformation logic to dbt, including orchestration, testing, documentation and CI/CD.
• Collaboration: Partner with Solution Architects to translate high-level designs into detailed implementation plans.
• Work closely with Technical Product Owners, media agencies and market teams to align data solutions with business needs.
• Platform Development: Enable data readiness for media dashboards (Power BI), marketing mix modelling / ROI analytics and campaign taxonomy governance.
• Implement robust frameworks to monitor data freshness, completeness, quality and pipeline performance, with automated alerting.
• Security & Compliance: Implement robust access controls and data protection across the pipeline and for data shared with internal and external consumers.
• Ensure compliance with data privacy regulations (e.g. GDPR, CCPA) and internal data governance policies (data product tiers, tagging, naming standards).
• Monitor and address potential vulnerabilities, including service account and secret management, ensuring the platform follows security best practices.

Requirements:

• 4+ years of experience showing technical expertise and critical thinking in data engineering.
• Hands-on experience with dbt and strong Python programming skills.
• Proficiency in Snowflake and strong expertise in data modeling are essential.
• Demonstrated experience with marketing, media or advertising data (ad platform APIs, campaign hierarchies, web analytics) is required.
• In-depth understanding of security and governance in Snowflake: RBAC, secure views, data sharing, masking and row access policies, object tagging.
• Ability to design, implement and promote solution patterns and standards for complex, multi-source data platforms.
• Familiarity with multiple cloud platforms (Azure or GCP preferred, with a focus on Azure, including ADF).
• Experience with ETL/ELT and data integration tools (Adverity, Fivetran or similar) and batch orchestration.
• Competence in SQL, relational database design and efficient methods for data retrieval and preparation at scale.
• Proven ability to collect and process raw data at scale, including scripting, REST API integration and SQL querying.
• Experience with BI tools (Power BI preferred) and supporting reporting consumers.
• Experience working in global environments and collaborating with virtual teams and external agencies.
• A Bachelor's or Master's degree in Data Science, Computer Science, Economics or a related discipline.

We are looking for a Mid Data Engineer in Kraków, Poland who will own the company data lake — the system of record for millions of financial events a day (bets, wallet movements, live odds) across 12M+ active users. You decide how that data lands, is stored, retained, and governed on S3 + Snowflake/Databricks, so analytics, finance, and regulators all see accurate, reconciled data with zero drift from source.

Domain: Regulated iGaming / wallet & ledger data. Audit-heavy: regulators, finance and analytics all consume the same tables. Millions of financial events per day, terabyte-plus scale.
 

Key Responsibilities

• Own lakehouse architecture, including Bronze/Silver/Gold layers, Iceberg/Delta tables, and schema evolution.
• Build CDC streaming pipelines using Kafka, Debezium, or similar technologies, handling late and duplicate events.
• Optimize data storage and query performance through partitioning, compaction, and file sizing.
• Manage data retention, archival, regulatory requirements, and GDPR deletion.
• Ensure data accuracy through freshness SLAs, drift detection, and reconciliation with wallet and ledger systems.
• Own data governance, including cataloging, lineage, access control, PII masking, encryption, and audit trails.
• Monitor ingestion health, data quality, and cloud storage/compute costs.

Requirements

• 5+ years of experience in Data Engineering, with proven ownership of large-scale data lake or lakehouse architectures.
• Production experience with cloud storage (S3/GCS) and Snowflake, Databricks, or BigQuery.
• Hands-on experience with open table formats (Iceberg, Delta, or Hudi).
• Strong understanding of partitioning, file layout, and query optimization at terabyte scale.
• Experience with CDC and streaming ingestion (Kafka, Debezium, or equivalent).
• Strong SQL and data modeling skills, with a solid understanding of relational databases and financial ledger systems.
• Experience with data governance, including catalogs, lineage, access control, PII, and retention.
• Python and workflow orchestration experience (Airflow, Dagster, or similar).

• Hands-on experience building and deploying AI agents and LLM-powered workflows to automate complex processes at scale.

   

  Nice to have: Experience in fintech, iGaming, or other regulated, audit-heavy industries.

You'll own the company's data lake, managing millions of financial events daily (bets, wallet movements, and live odds) across 12M+ active users. You'll define how data is ingested, stored, retained, and governed across S3 and Snowflake/Databricks, ensuring accurate, reconciled, and reliable data for analytics, finance, and regulatory needs.

Key Responsibilities

• Own lakehouse architecture, including Bronze/Silver/Gold layers, Iceberg/Delta tables, and schema evolution.
• Build CDC streaming pipelines using Kafka, Debezium, or similar technologies, handling late and duplicate events.
• Optimize data storage and query performance through partitioning, compaction, and file sizing.
• Manage data retention, archival, regulatory requirements, and GDPR deletion.
• Ensure data accuracy through freshness SLAs, drift detection, and reconciliation with wallet and ledger systems.
• Own data governance, including cataloging, lineage, access control, PII masking, encryption, and audit trails.
• Monitor ingestion health, data quality, and cloud storage/compute costs.

Requirements

• 5+ years of experience in Data Engineering, with proven ownership of large-scale data lake or lakehouse architectures.
• Production experience with cloud storage (S3/GCS) and Snowflake, Databricks, or BigQuery.
• Hands-on experience with open table formats (Iceberg, Delta, or Hudi).
• Strong understanding of partitioning, file layout, and query optimization at terabyte scale.
• Experience with CDC and streaming ingestion (Kafka, Debezium, or equivalent).
• Strong SQL and data modeling skills, with a solid understanding of relational databases and financial ledger systems.
• Experience with data governance, including catalogs, lineage, access control, PII, and retention.
• Python and workflow orchestration experience (Airflow, Dagster, or similar).
• Hands-on experience building and deploying AI agents and LLM-powered workflows to automate complex processes at scale.
• Nice to have: Experience in fintech, iGaming, or other regulated, audit-heavy industries.