Jobs Data Engineer

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

Requirements

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

Will be a plus

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

Responsibilities

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

What we offer:

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

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

Requirements

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

Will be a plus

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

Responsibilities

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

What we offer:

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

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.

About This Role:

We're looking for a Senior, dbt-specialised Analytics Engineer to build and maintain the business-critical data models behind the client's credit, payments, and fraud/AML reporting. Our client is a fast-growing European fintech company in the business spend management space — corporate cards and related financial products — serving SME and mid-sized business customers across the EU and UK, in a regulated environment with GDPR compliance obligations. Engineering is organized into cross-functional squads, with platform/enabling teams providing shared tooling horizontally. The client already runs an AI-native engineering practice: Claude Code and GitHub Copilot are used daily, supported by a growing library of shared, versioned Skills embedded in key repositories, enabling an end-to-end flow from ticket to implementation, testing and PR in several codebases. AI tool/connector rollout follows an approved-list and pilot process. The client is AWS-first overall; for data platform and analytics workloads it also runs GCP, with BigQuery as the primary data warehouse. Day-to-day coordination is Slack-first, with Linear for ticket tracking, GitHub for code/PR review, and Notion as the knowledge base.

Key Responsibilities:

• Build and maintain business-critical dbt data models.
• Model data powering credit, payments, and fraud/AML reporting (working from aggregated/modeled data, not raw access to real customer/transaction data — see note above).
• Apply strong SQL and data-modelling skills.
• Multiply the output and quality of the squad you join, and share patterns/practice beyond your immediate team so adoption compounds across the organisation.
• Embed directly into a client squad as a hands-on Individual Contributor (not a coaching/managerial role).

Must-Have Skills:

• Experience with dbt.
• Strong SQL and data-modelling skills.
• Experience building and maintaining business-critical data models for credit, payments, and fraud/AML reporting.
• BigQuery — the client's stated primary data warehouse for data platform and analytics workloads.
• Experience with fintech domain. 

Nice-to-Have Skills:

• Git — standard, expected tooling, but not explicitly named in the RFP for this role.

Join an exciting journey to create a cutting-edge Consumer Data Lake for a leading global organization based in Europe. This platform will unify, process, and leverage consumer data from various systems, unlocking advanced analytics, insights, and personalization opportunities. As a Senior Data Engineer, you will play a pivotal role in shaping and implementing the platform's architecture, focusing on hands-on technical execution and collaboration with cross-functional teams.

Your work will transform consumer data into actionable insights and personalization on a global scale. Using advanced tools to tackle complex challenges, you’ll innovate within a collaborative environment alongside skilled architects, engineers, and leaders.

Key Responsibilities:

• Hands-On Development: Build, maintain, and optimize data pipelines for ingestion, transformation, and activation.
• Create and implement scalable solutions to handle diverse data sources and high volumes of information
• Data Modeling & Warehousing: Design and maintain efficient data models and schemas for a cloud-based data platform.
• Develop pipelines to ensure data accuracy, integrity, and accessibility for downstream analytics.
• Collaboration: Partner with Solution Architects to translate high-level designs into detailed implementation plans.
• Work closely with Technical Product Owners to align data solutions with business needs.
• Collaborate with global teams to integrate data from diverse platforms, ensuring scalability, security, and accuracy.
• Platform Development: Enable data readiness for advanced analytics, reporting, and segmentation.
• Implement robust frameworks to monitor data quality, accuracy, and performance.
• Testing & Quality Assurance: Implement robust security measures to protect sensitive consumer data at every stage of the pipeline
• Ensure compliance with data privacy regulations (e.g., GDPR, CCPA ..) and internal policies.
• Monitor and address potential vulnerabilities, ensuring the platform adheres to security best practices.

Requirements:

• Over 4+ years of experience showcasing technical expertise and critical thinking in data engineering.
• Hands-on experience with DBT and strong Python programming skills.
• Proficiency in Snowflake and expertise in data modeling are essential.
• Demonstrated experience in building consumer data lakes and developing consumer analytics capabilities is required.
• In-depth understanding of privacy and security engineering within Snowflake, including concepts like RBAC, dynamic/tag-based data masking, row-level security/access policies, and secure views.
• Ability to design, implement, and promote advanced solution patterns and standards for solving complex challenges.
• Familiarity with multiple cloud platforms (Azure or GCP preferred, with a focus on Azure).
• Practical experience with Big Data batch and streaming tools.
• Competence in SQL, NoSQL, relational database design (SAP HANA experience is a bonus), and efficient methods for data retrieval and preparation at scale.
• Proven ability to collect and process raw data at scale, including scripting, web scraping, API integration, and SQL querying.
• Experience working in global environments and collaborating with virtual teams.
• A Bachelor’s or Master’s degree in Data Science, Computer Science, Economics, or a related discipline.

We are expanding our talent pool of Python Data Engineers to support upcoming international projects across various industries, including FinTech, Healthcare, Retail, Logistics, and SaaS.

While there is no immediate project assignment, selected candidates will become part of our network and will be among the first to be considered when new opportunities matching their expertise become available.

Our data engineering projects typically involve designing scalable data pipelines, building ETL/ELT processes, integrating multiple data sources, and developing modern cloud-based data platforms using Python and industry-leading technologies.

As a Data Engineer on the Enterprise Data & Intelligence team, you will design, build, and maintain the data pipelines and models that underpin Fourth’s single source of truth. Reporting to the Senior Data Engineer, you will gather and translate business requirements into reliable, well-tested data solutions on our Azure-based cloud platform. You will collaborate closely with analytics engineers and business stakeholders to scope, prioritise, and deliver data products that serve FP&A, Commercial, Product, and AI teams.

Responsibilities:

1. Data Pipeline Development & Optimisation

•Build, maintain, and optimise scalable ETL/ELT pipelines (batch and near-real-time) on Azure Data Cloud Platform (e.g., Data Lake, Microsoft Fabric, Azure Data Factory).

• Develop and refine data models to support BI reporting, analytics, and ML/AI use cases.

•Write efficient, well-documented T-SQL and PySpark code following team coding standards.

2. Data Quality, Testing & Governance

• Implement automated testing, data validation, and monitoring (SLAs, alerts) to ensure pipeline reliability.

• Contribute to data governance practices, including lineage tracking, metadata management, and quality controls.

•Support CI/CD pipelines for data assets, ensuring version control and reproducibility.

3. Requirements Scoping & Cross-Functional Collaboration

•Partner with analytics engineers to scope, refine, and prioritise data requirements from business stakeholders.

•Work with Analysts, BI Developers, Data Scientists, and business teams to translate requirements into production-ready data solutions.

•Provide input on data readiness for machine learning and analytics projects.

4. Platform Contribution & Continuous Improvement

• Contribute to the evolution of the ED&I data platform, including tooling, standards, and documentation.

• Stay current with emerging data engineering patterns and technologies; propose improvements to team processes.

• Leverage AI-driven development tools (e.g., generative-AI code assistants, automated data profiling) to accelerate delivery.

• Support performance tuning and cost optimisation across the data platform.

Requirements:

Experience: 3+ years in data engineering or a closely related role.

Education: Bachelor’s degree in Computer Science, Data Engineering, or a related field.

Technical Skills:

○Strong T-SQL skills and working proficiency in PySpark or Python for data processing.

○Hands-on experience with MS Azure Storage Explorer and SSMS.

○Hands-on experience with cloud-based data engineering services and orchestration tools (e.g., Azure Data Factory, Microsoft Fabric).

○Practical experience building ETL/ELT pipelines and dimensional or analytical data models.

○Familiarity with CI/CD practices in data engineering, including version control (Git) and automated testing.

Preferred Qualifications
• Experience with real-time or streaming data architectures.
• Experience with PowerShell, Apache Kafka, and/or KQL.
• Exposure to AI/ML workflows (feature engineering, data preparation for model training).
• Familiarity with Power BI or other BI/visualisation tools.
• Experience using AI productivity tools (e.g., ChatGPT, Claude, Copilot, Cursor) in day-to-day and data engineering tasks.
• Understanding of data security, privacy, and compliance considerations.

We offer*:

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

*not applicable for freelancers
 

We're looking for a Middle+ Data/Databricks Engineer to join our data team and take ownership of pipelines built on Databricks. You'll design and run production-grade ETL/ELT workloads, work with the lakehouse (Delta Lake) architecture, and partner closely with analysts, data scientists, and business stakeholders who rely on the data you deliver. "Middle+" here means roughly 3–5 years of hands-on data engineering experience: you can own a pipeline end to end with minimal supervision, make sound design decisions, and are starting to mentor more junior engineers — without yet carrying full architectural ownership of the platform.

Contract & Logistics

• Contract type: B2B (valid Czech IČO / trade license required).
• Location: Remote, candidate must be based in the Czech Republic; occasional visit to Prague office.
• Start date: ASAP
• English: Fluent 
• Czech: Native/Fluent 

What You'll Do

• Design, build, and maintain scalable ETL/ELT pipelines on Databricks using PySpark and SQL.
• Implement and evolve Delta Lake tables following a medallion (bronze/silver/gold) architecture.
• Develop batch and, where needed, streaming data processing jobs (Structured Streaming).
• Build and maintain CI/CD for Databricks notebooks and jobs (Databricks Asset Bundles, Repos, Git-based workflows).
• Orchestrate pipelines with Databricks Workflows, Azure Data Factory, Airflow, or similar tools.
• Monitor job performance and cluster costs; troubleshoot failures and optimize for reliability and spend.
• Implement data quality checks, testing, and monitoring for critical datasets.
• Collaborate with analysts, data scientists, and business stakeholders to translate requirements into data models.
• Participate in code reviews, document technical decisions, and help shape engineering best practices.
• Provide guidance and informal mentorship to junior team members.

What We're Looking For

• 3+ years of hands-on experience with Apache Spark, ideally on Databricks specifically.
• Strong PySpark and SQL skills, including performance tuning of Spark jobs.
• Practical experience with Delta Lake and lakehouse architecture concepts.
• Solid grasp of data modeling (dimensional modeling, star schema) and ETL/ELT design patterns.
• Experience with at least one major cloud platform — Azure, AWS, or GCP (Azure Databricks is a plus).
• Familiarity with pipeline orchestration tools (Databricks Workflows, ADF, Airflow, or equivalent).
• Working knowledge of Git and CI/CD practices for data engineering.
• Understanding of data governance, access control, and security basics (Unity Catalog is a plus).
• Good written and spoken English; Czech or Slovak is a plus but not required.
• Comfortable working independently and communicating clearly in a remote, distributed team.

Nice to Have

• Databricks certification (Data Engineer Associate or Professional).
• Experience with streaming technologies such as Kafka or Azure Event Hubs.
• Experience with dbt for transformation and testing.
• Solid Python software-engineering practices — testing, packaging, typing.
• Infrastructure-as-code experience (Terraform).
• Exposure to MLOps or ML pipeline support.
• Experience in a regulated or large enterprise data environment.

What We Offer

• Fully remote work with flexible hours, based anywhere in the Czech Republic.
• Long-term B2B cooperation with a stable pipeline of work.
• A modern, actively evolving data stack rather than legacy maintenance work.
• Direct collaboration with senior engineers and architects — real input on design decisions.
• Budget for training, certifications, and conference attendance.
• A flat structure with fast decision-making and minimal bureaucracy.

We are looking for an experienced Data Engineer to design and maintain robust data infrastructure across our systems. In this role, you will be responsible for building scalable data pipelines, ensuring data integrity, and integrating third-party data sources. Your primary focus will be to enable efficient data flow and support analytical capabilities across the organization. You will also contribute to the development of our data architecture, implement best engineering practices, and collaborate closely with cross-functional teams to turn raw data into actionable insights.

Requirements

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

Will be a plus

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

Responsibilities

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

What we offer:

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

Join Sigma Software to build large-scale data infrastructure powering a real-time AdTech platform processing hundreds of millions of auction requests daily. We are looking for a Senior Data Engineer who enjoys solving complex distributed data challenges and building production-grade ML-oriented data systems.

You will become part of a dedicated Sigma Software team developing predictive modeling and optimization capabilities for a live advertising ecosystem. The role combines large-scale event processing, streaming and batch pipelines, experimentation infrastructure, and high-throughput data engineering in a cloud-native environment.

We as a company offer the opportunity to work on impactful global products, collaborate with experienced engineers, and contribute to architecture decisions while growing your expertise in large-scale distributed systems and modern data platforms.

Customer

Our Customer is a technology company operating supply-side infrastructure within the programmatic advertising ecosystem. The company manages a large-scale ad exchange handling hundreds of millions of auction requests per day and is actively investing in predictive decisioning technologies to optimize advertising outcomes in real time.

Project

The project focuses on building a predictive modeling and optimization platform on top of a live ad exchange environment. The platform performs real-time supply scoring and filtering, contextual performance estimation, look-alike audience generation, and multi-objective optimization under business constraints.

The solution processes massive-scale event and auction datasets and includes feature engineering pipelines, streaming and batch ingestion, experimentation infrastructure, point-in-time-correct training data generation, and ML-oriented data services with strict operational reliability and compliance requirements.

Requirements

• 5+ years of experience in Data Engineering
• At least 2 years of experience working with production ML or large-scale analytics pipelines
• Expert-level SQL skills including window functions and incremental processing patterns
• Strong Python skills for production-grade pipeline development
• Hands-on experience with Spark or PySpark
• Experience designing ETL / ELT pipelines with Airflow, Cloud Composer, Dagster, or similar tools
• Experience working with cloud data warehouses at scale, preferably BigQuery
• Strong understanding of data modeling and point-in-time correctness
• Experience working with event-driven or clickstream datasets at very large scale
• Experience supporting business-critical production pipelines

• Upper-Intermediate English level or higher

   

WILL BE A PLUS

• Experience with GCP services including Dataflow, Pub/Sub, GCS, and Beam
• Experience building streaming or near-real-time ingestion systems
• Understanding of feature stores, train/serve skew, and label leakage prevention
• Experience in AdTech or auction-based environments
• Experience handling delayed or incomplete labels in ML systems
• Experience with dbt or similar transformation frameworks
• Experience delivering solutions into Customer-owned infrastructure
• Knowledge of GDPR/CCPA-related privacy engineering practices
• Experience with experimentation infrastructure and statistical validation pipelines
• Experience working in hybrid cloud/on-prem Linux environments
• Terraform and Kubernetes experience

• Experience optimizing warehouse cost and performance

   

Personal Profile

• Strong analytical and problem-solving skills
• Ownership-oriented mindset
• Ability to work independently in a client-facing environment
• Strong communication and documentation skills
• Comfortable working in a fast-paced engineering environment
• Collaborative and proactive attitude

Responsibilities

• Write and defend diagnostic SQL queries against large-scale production datasets
• Build and maintain ingestion pipelines for bid, win, and impression logs into BigQuery
• Harmonize fields across independently designed datasets and maintain versioned field mappings
• Develop point-in-time-correct feature tables and aggregation pipelines
• Design and maintain conversion and labeling pipelines with delayed label handling
• Own the data serving write path, schema contracts, publishing flows, and freshness SLOs
• Build experimentation infrastructure including traffic splitting and reporting pipelines
• Perform large-scale historical backfills and safe reprocessing after mapping changes
• Implement data isolation and safe-aggregation controls for advertiser data protection
• Develop automated data quality validation frameworks
• Collaborate closely with Customer engineers and prepare operational documentation
• Contribute to architecture discussions and platform scalability improvements

We are looking for 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.
 

What you'll be doing:

• Own the lakehouse architecture: bronze/silver/gold layers, Iceberg/Delta tables, schema evolution.
• Land operational data via CDC streaming (Kafka, Debezium), handling late and duplicate events.
• Design data layout for speed and cost: partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.
• Own retention and archival: storage tiering, regulatory retention, immutability, GDPR deletion.
• Guarantee correctness: freshness SLAs, drift detection, reconciliation against the source wallet and ledger systems.
• Own governance: catalog and lineage, row/column access control, PII masking, encryption, audit trails.
• Monitor ingestion health, data anomalies, and cloud storage/compute spend.
  
  Must-have:
• 3 years' experience in hands-on delivery within that architecture — pipelines, ingestion, models, monitoring
• Lakehouse architecture — bronze/silver/gold layering, an open table format (Iceberg, Delta, or Hudi), schema evolution.
• Data layout & query optimization at TB+ scale — partitioning, compaction, file sizing, query performance on Trino/Athena/Snowflake.
• Cloud lakehouse/DWH in production — Snowflake, Databricks, or BigQuery.
• CDC & streaming ingestion — Kafka + Debezium or equivalent; late, duplicate and out-of-order events.
• Strong SQL and data modeling — enough relational grounding to reason about the OLTP systems you capture from. Critical for financial ledgers.
• Correctness — freshness SLAs, drift detection, reconciliation against source wallet/ledger systems.
• Governance — catalogs, lineage, row/column access control, PII masking, retention, GDPR deletion.
• Cloud object storage — S3 or GCS, plus storage tiering and archival.

• Python and an orchestrator — Airflow or Dagster, as tools.

   

  Location & work model:

• Kraków, Poland. Hybrid — 2 days per week from the office.

We are looking for an experienced DevOps Engineer to take ownership of production infrastructure, cloud environments, Kubernetes platforms, and infrastructure automation. This is a hands-on role for someone who enjoys solving complex infrastructure challenges and is comfortable being responsible for systems in production.

Key Responsibilities

• Own and operate production infrastructure, including participating in an on-call rotation and responding to production incidents.
• Design, operate, and continuously improve Kubernetes clusters in production.
• Manage and automate infrastructure using Infrastructure as Code, primarily with Terraform.
• Build, maintain, and optimise cloud infrastructure across AWS, GCP, or Azure.
• Work extensively with Linux, including system administration, networking, troubleshooting, and system-level configuration.
• Manage production deployment and GitOps workflows using ArgoCD.
• Improve infrastructure reliability, scalability, security, monitoring, and operational efficiency.
• Troubleshoot complex production issues and drive problems through to resolution.
• Develop automation and processes that reduce manual operational work.

Essential Requirements

• 4+ years of hands-on experience operating production infrastructure, with personal ownership and responsibility for live systems, including on-call experience.
• Deep, hands-on Kubernetes experience — you must have operated and managed Kubernetes clusters, rather than simply deploying applications onto clusters managed by another team.
• Strong experience with Infrastructure as Code, with Terraform strongly preferred. Experience with Pulumi or CloudFormation is also considered.
• Strong experience with at least one major cloud platform, ideally AWS. Strong GCP or Azure experience is also welcome, provided you are willing to work with AWS.
• Strong Linux skills and confidence working from the command line, including networking, troubleshooting, system configuration, and performance issues.
• Production experience with ArgoCD and GitOps-based deployment workflows.
• Strong troubleshooting and problem-solving skills, with the ability to take ownership of production incidents and infrastructure issues.

Nice to Have

Experience with email infrastructure would be a strong advantage, particularly:

• Mailcow
• IMAP / SMTP
• Postfix
• Dovecot
• General mail server administration and maintenance

What We're Looking For

We're looking for someone who is genuinely hands-on and operationally experienced. You should be comfortable owning infrastructure end to end, investigating issues directly on production systems, and making decisions that improve reliability and scalability.

If you've built and operated Kubernetes and cloud infrastructure yourself, enjoy automation, and are comfortable getting deep into Linux and production troubleshooting, we'd love to hear from you.

What is the purpose of this position?
We are looking for a Senior Data Engineer with strong hands-on experience in the Azure ecosystem to design, build, and maintain scalable data pipelines and infrastructure. You will work closely with other engineers to ensure reliable, high-quality data flows across the organization. Potential clients make many requests, so we want to bring a new specialist on board and provide fast staffing.

Location: Remote (CET time zone preferably), ready to coordinate with US and EU time zones.

Responsibilities:
Design, build, and maintain scalable, reliable ETL/ELT data pipelines for batch and streaming data.

• Architect and optimize data storage solutions (data lakes, data warehouses) on Azure.
• Develop and maintain data models, schemas, and transformation logic to support analytics and reporting.
• Ensure data quality, consistency, and integrity across pipelines through validation, monitoring, and alerting.
• Optimize pipeline performance and troubleshoot bottlenecks in large-scale data processing.
• Implement and maintain CI/CD pipelines for data engineering workflows.
• Collaborate with data scientists, analysts, and backend engineers to understand data requirements and deliver solutions.
• Define and enforce best practices for data governance, security, and access control.
• Mentor junior data engineers and contribute to technical decision-making and architecture reviews.
• Document data architecture, pipelines, and processes for internal knowledge sharing.
   

Requirements:

• Strong hands-on experience as a Data Engineer, including senior-level or lead responsibilities.
• Strong practical experience with the Azure data ecosystem (e.g. Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure Data Lake Storage).
• Strong knowledge of SQL and experience with relational and non-relational databases.
• Strong programming experience in Python (or Scala/Java) for data processing and pipeline development.
• Experience designing and building ETL/ELT pipelines for both batch and streaming data (e.g. Spark, Kafka, Azure Event Hubs).
• Solid understanding of data modeling, data warehousing concepts, and dimensional design.
• Experience with infrastructure-as-code and CI/CD tools (e.g. Terraform, ARM/Bicep templates, Azure DevOps, GitHub Actions).
• Experience with data quality, testing, and monitoring practices for data pipelines.
• Strong analytical and problem-solving skills, with the ability to work independently on ambiguous or complex data challenges.
• Strong communication skills, with the ability to collaborate cross-functionally and explain technical concepts to non-technical stakeholders.
   

Nice to Have:

• Azure certifications (e.g. Azure Data Engineer Associate, Azure Solutions Architect).
• Experience with orchestration tools (e.g. Apache Airflow, Azure Data Factory pipelines).
• Experience with data governance and cataloging tools (e.g. Microsoft Purview).
• Familiarity with BI/reporting tools (e.g. Power BI).

• Experience working in a regulated or high-compliance data environment.
   

  Hiring process:
  • HR interview
  • Technical interview
  • Client interviews
  • Reference check
  
  We offer:
  • Remote Work Environment. Work from anywhere and be part of a geographically diverse team
  • Stay Ahead of the Curve. Work with cutting-edge technologies
  • Possibility of payment via PE account, Payoneer, or Wise
  • Bonus & Referral system
  • 16 paid vacation days and 10 sick days per year
  • Flexible hours
  • Compensation program for purchasing new laptops
  • Paid professional certifications & educational courses
  • Paid English classes
  • Friendly atmosphere with quarterly team building
  • No bureaucracy
  
  Ready to try your hand? Do not pull the cat’s tail and send your CV without a doubt!

Are you passionate about building cutting-edge, AI-ready data platforms from the ground up? We are looking for a Senior Data Engineer to join our Data Engineering Team and lead high-impact, greenfield initiatives.

You will work on building modern cloud-native data platforms, migrating on-premises legacy systems to the cloud, and laying the architectural foundation for AI-ready data infrastructure.

In this role, you will collaborate closely with Machine Learning, Data Science, and Product teams, serving as a key technical contributor and thought leader. You will also drive R&D efforts around agentic AI architectures, event-driven systems, and LLM-ready data pipelines — turning architectural concepts into production-grade solutions.

Responsibilities:

• Design and build scalable, cloud-native data platforms from greenfield to production
• Implement near-real-time ingestion pipelines using event-driven patterns
• Define and enforce platform standards, including Data Lake / Lakehouse principles, medallion architecture, and data contracts
• Refactor and optimise existing Spark and PySpark scripts for performance and maintainability
• Introduce best practices for code quality, testing, and CI/CD across data pipelines
• Drive adoption of AI tooling and agentic workflows within the data engineering team
• Ensure data quality, observability, and reliability across all pipelines and platforms

• Develop self-service tooling and microservices to simplify platform usage for other teams

   

Requirements:

• 5+ years of professional experience in Data Engineering
• Strong Python and SQL development skills for pipeline development and optimisation
• Proficiency in Apache Spark / PySpark, including query optimisation and performance tuning
• Hands-on experience with Databricks 
• Experience with at least one major cloud provider: Azure (preferred), AWS, or GCP
• Experience with stream processing technologies (Kafka, Spark Structured Streaming)
• Solid understanding of ETL/ELT patterns, data modelling (dimensional, Data Vault), and data warehousing
• Experience with orchestration tools (Apache Airflow, Azure Data Factory, or equivalent)
• Knowledge of Infrastructure as Code (Terraform or equivalent)
• Understanding of production-grade system requirements: reliability, scalability, observability, and performance

• Upper-Intermediate English level

   

Will be a plus

• Familiarity with RAG pipeline design and LLM integration patterns
• Knowledge of data governance frameworks and tools (Unity Catalog, Apache Atlas, or similar)
• Experience with dbt for data transformation and modelling

• Familiarity with MLflow, Feature Stores, or ML platform integration

   

Personal profile

• Self-driven and proactive in identifying improvements
• Comfortable working in a fast-paced, innovative environment
• Strong problem-solving mindset with attention to detail
• Open to experimenting with emerging technologies and approaches

_____________________________________________________________

Ти захоплюєшся створенням передових, AI-ready дата-платформ з нуля? Ми шукаємо Senior Data Engineer, щоб ти приєднався(-лася) до нашої команди Data Engineering та очолив(-ла) масштабні, інноваційні ініціативи з нуля.

Ти працюватимеш над побудовою сучасних хмарних дата-платформ, міграцією локальних (on-premises) легасі-систем у хмару та створенням архітектурного фундаменту для AI-ready дата-інфраструктури.

У цій ролі ти тісно співпрацюватимеш з командами Machine Learning, Data Science та Product, виступаючи ключовим технічним експертом та лідером думок. Також ти вестимеш R&D-роботи над agentic AI-архітектурами, подієво-орієнтованими системами та LLM-ready дата-пайплайнами — перетворюючи архітектурні концепції на готові до продакшену рішення.

Обовʼязки:

• Проєктувати та створювати масштабовані, хмарні дата-платформи від концепції до продакшену
• Реалізовувати пайплайни для майже реального часу (near-real-time ingestion) з використанням подієво-орієнтованих патернів
• Визначати та впроваджувати стандарти платформи, включно з принципами Data Lake / Lakehouse, медальйонною архітектурою та дата-контрактами
• Рефакторити та оптимізувати наявні скрипти на Spark та PySpark для підвищення продуктивності та зручності підтримки
• Запроваджувати найкращі практики якості коду, тестування та CI/CD для дата-пайплайнів
• Сприяти впровадженню AI-інструментів та agentic-підходів у команді дата-інженерів
• Забезпечувати якість, спостережуваність (observability) та надійність усіх пайплайнів і платформ

• Розробляти self-service інструменти та мікросервіси, що спрощують використання платформи іншими командами

   

Вимоги:

• 5+ років комерційного досвіду в Data Engineering
• Сильні навички розробки на Python та SQL для створення та оптимізації пайплайнів
• Впевнене володіння Apache Spark / PySpark, включно з оптимізацією запитів та налаштуванням продуктивності
• Практичний досвід роботи з Databricks (бажано) 
• Досвід роботи з принаймні одним великим хмарним провайдером: Azure (бажано), AWS або GCP
• Досвід роботи з технологіями потокової обробки даних (Kafka, Spark Structured Streaming)
• Глибоке розуміння ETL/ELT-патернів, моделювання даних (dimensional, Data Vault) та побудови дата-складів
• Досвід роботи з інструментами оркестрації (Apache Airflow, Azure Data Factory або аналогами)
• Знання Infrastructure as Code (Terraform або аналогів)
• Розуміння вимог до продакшен-рішень: надійність, масштабованість, спостережуваність, продуктивність

• Рівень англійської — Upper-Intermediate

   

Буде плюсом:

• Знайомство з проєктуванням RAG-пайплайнів та патернами інтеграції LLM
• Знання фреймворків і інструментів для data governance (Unity Catalog, Apache Atlas або подібних)
• Досвід роботи з dbt для трансформації та моделювання даних

• Знайомство з MLflow, Feature Stores або інтеграцією з ML-платформами

   

Особистий профіль:

• Самомотивованість та проактивність у пошуку покращень
• Комфортна робота у швидкому та інноваційному середовищі
• Сильне аналітичне мислення та увага до деталей
• Відкритість до експериментів з новими технологіями та підходами

We are looking for a Senior BI Engineer (Looker/LookML) to join our team!

Our client is a US-based company specializing in supporting the HR needs of the US public sector by automating and enhancing HR lifecycle management. With 20+ years of industry experience, they provide solutions for public sector organizations ranging from 100 to 60,000 employees. The project focuses on migrating and enhancing the client reporting infrastructure to the cloud.

Responsibilities:

• Enhance and optimize existing BI reports in Looker, covering solutions of varying complexity.
• Design, build, and maintain robust BI dashboards and reports in Looker based on business requirements.
• Work with existing LookML models, explores, and dashboards to deliver reporting enhancements and new analytical solutions.
• Collaborate closely with the client’s BI and engineering teams to understand existing data structures and ensure consistency across reporting solutions.
• Engage in regular communication with stakeholders to gather requirements, resolve data and reporting challenges, and ensure a clear understanding of business needs.
• Demonstrate a proactive position in identifying potential risks, proposing improvements, and taking initiative to drive tasks to completion.
• Ensure data quality and accuracy across all analytical outputs.
• Provide technical guidance and best practices on Looker and BI development.

Requirements:

• Proven hands-on experience with Looker and LookML, including development, enhancement, and optimization of existing reports and dashboards.
• Strong knowledge of BI concepts, data modeling, ETL, and data warehousing principles.
• Proficiency in SQL and experience working with large data sets.
• Hands-on experience with Git and version control workflows.
• Excellent communication skills and ability to work collaboratively in a cross-functional team environment.
• Proactive attitude and ownership over deliverables.
• English: Intermediate-strong or higher, with a proven ability to articulate professional solutions to a foreign client.

Nice to have:

• Experience migrating on-prem BI solutions to cloud environments.
• Exposure to the public sector or HR solutions is a plus.
• Experience working with Snowflake, with an understanding of its specific features and differences from other database and cloud data warehouse technologies.
• Familiarity with other cloud data warehouses and modern cloud data stacks, such as BigQuery, Redshift, or similar technologies.

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