Jobs Data Engineer

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

Responsibilities:

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

Requirements:

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

Nice to have:

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

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

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

Responsibilities:

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

Requirements:

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

Nice to have:

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

Conditions:

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

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

Why join us

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

About the role

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

The mandatory requirements are 2+ years of experience with Databricks and GCP, advanced SQL, and experience with Delta Lake and lakehouse architectures, along with an upper-intermediate English level. Experience building AI agents is a plus.

What you will do

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

Must haves

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

Nice to haves

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

Perks

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

Meet Our Recruitment Process

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

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

Synchronous stage – Live interviews

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

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

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

About the client

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

About the role

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

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

Responsibilities:

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

Requirements:

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

Nice to have

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

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

About the client

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

About the role

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

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

Responsibilities:

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

Requirements:

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

Nice to have

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

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

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

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

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

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

Responsibilities

• Design, implement, and manage cloud infrastructure: Azure (AWS is a plus).
• Build and maintain docker swarm servers, Azure and AWS services.
• Develop Infrastructure as Code (IaC) solutions using Terraform (CloudFormation is a plus).
• CI/CD pipelines: Azure DevOps, Bitbucket pipelines, GitHub Actions.
• Monitoring and alerting solutions: ELKEFK stacks, Grafana, Prometheus.
• Collaborate with development teams on automation, deployment, and infrastructure needs.
• Responsible for IT services in the company

Requirements

• Understanding of DevOps principles, culture and CI/CD pipelines.
• Knowledge of Docker Compose, Docker Swarm (Kubernetes is a plus).
• Proven expertise with Azure (App Service, VM, Storage, Office365).
• Experience with database administration and monitoring: PostgreSQL, MS SQL
• Experience with web servers and load balancers: Nginx, Apache, Traefik; cloud load balancers
• Working experience 3+ years in servers’ administration based on Windows, Linux
• Experience with monitoring systems: Grafana, Prometheus, EFK/ELK stack.
• Version control skills (AzureDevOps, Bitbucket, GitHub).
• Knowledge and experience with TCP/IP, VPN, a configuration of Firewall
• Experience in troubleshooting and performance tuning
• Ability to document processes and decisions

We Offer

• Competitive salary
• Flexible schedule
• Social package
• Courses, webinars, and seminars to increase professional skills
• Possibility to work remotely and onsite
• Horizontal hierarchy. No micromanagement pressure
• Open company culture where your ideas and initiatives are valued
• Regular team building & corporate events
• International projects

Position Overview

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

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

Responsibilities

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

Required Skills

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

Nice to Have

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

What We Expect

We do not expect a Junior engineer to know everything.

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

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

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

About the Client

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

About the Role

We are looking for a Middle-level Software Engineer to work on the core query engine of a large-scale distributed data platform. You will develop features across query planning, optimization, and execution, contribute to performance-critical components, and help evolve the platform's ability to process massive analytical workloads across heterogeneous data sources.

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

Responsibilities

• Develop and maintain features across the query engine — planning, optimization, execution, and data access layers.
• Write high-quality, performance-conscious code in Java and/or C++.
• Work with SQL semantics, query plans, and execution operators over large-scale distributed data.
• Integrate with columnar formats, open table formats, and connectivity drivers used across the platform.
• Contribute to CI/CD pipelines and automated testing — participate in build, test, and release workflows in Jenkins.
• Deploy and validate work in Kubernetes (GKE / EKS / AKS) across GCP, AWS, and Azure using Docker.
• Debug complex issues spanning query planning, distributed execution, memory management, and I/O.
• Collaborate closely with US-based engineering teams on design reviews, code reviews, and technical decisions.

Required Qualifications

• Education: B.S. or M.S. in Computer Science, Computer Engineering, or a related technical field.
• Programming: Strong proficiency in Java or C++, with solid OOP and software design fundamentals. Candidates comfortable with both are especially valued.
• SQL & Data: Strong SQL skills and solid understanding of relational and analytical data systems, including query execution concepts.
• Data processing systems experience: Hands-on experience with data processing systems — query engines, distributed databases, ETL/ELT frameworks, or analytical platforms.
• Engineering experience: 3+ years in backend/systems software engineering.
• CI/CD & DevOps basics: Practical experience with Jenkins pipelines and modern development workflows.
• Containers: Working knowledge of Docker and basic Kubernetes (running workloads, debugging pods, kubectl fluency).
• Cloud: Comfortable operating in at least one major cloud (GCP, AWS, or Azure).
• Version Control: Confident with Git / GitHub workflows.
• English: Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team.
• Availability: Able to work EU hours with a 2–3 hour shift toward US West Coast time to ensure daily overlap with the client team.

Desired Skills

• Experience with Apache Arrow (columnar in-memory format).
• Familiarity with Apache Calcite (SQL parsing, planning, optimization framework).
• Exposure to Gandiva or LLVM-based expression compilation.
• Experience with open table formats — Apache Iceberg, Delta Lake, or Hudi.
• Familiarity with distributed computing frameworks (e.g., Apache Spark, Kafka) and MPP SQL query engines (e.g., Presto, Trino, or similar).
• Understanding of query planning, optimization, and execution internals.
• Experience with data connectivity drivers: JDBC, ODBC, Arrow Flight.
• Kubernetes on managed services (GKE / EKS / AKS) and multi-cloud exposure.
• IaC tools such as Terraform.
• Experience with performance profiling and optimization of latency-sensitive systems.
  Please note: We can consider only candidates currently residing in the EU, EEA, or the UK.
  Please note: We can consider only candidates currently residing in the EU, EEA, or the UK

We're looking for an AI & Data Engineer to build the data and AI backbone of a livestream shopping and social marketplace platform for the Middle East - one that combines real-time auctions and social engagement into a single automated, data-driven product.

The audience pipelines, personalization logic and AI agents for this platform don't exist yet. You build them from scratch and own how they run in production.

What you'll do

Growth Data Activation (MarTech)

• Build automated audience pipelines from the warehouse into marketing platforms - Meta CAPI, Google Ads, Braze, Customer.io, WhatsApp API.

Lifecycle Automation & Personalization

• Design behavior-based triggers (cart abandonment, livestream reminders) and ranking/recommendation logic for products and live shows.

Practical AI Integration

• Deploy production-ready LLM workflows and AI agents for operational needs - seller onboarding, content moderation, automated customer support - with proper cost and evaluation guardrails.

Who we're looking for

Must have:

• 3-4 years of experience in Data Engineering, Software Engineering, or Data Architecture.
• Strong, production-grade Python and SQL.
• Proven experience building OLAP data warehouses (ClickHouse, BigQuery, Snowflake, Redshift etc)
• Experience with event tracking and identity resolution - Segment, RudderStack, PostHog.
• Practical experience integrating LLMs/RAG into real production systems - not just prompts or demos.

Backend / Data Infrastructure Engineer

We are a technology startup in the field of Data Infrastructure and Cloud Analytics. We are developing a platform that optimizes analytical query execution by automatically selecting the most efficient query engine based on speed, cost, and performance.

Employment: Full-time, Remote

Core Technologies

-Python

-Rust

-SQL

-Snowflake

-DuckDB

-Trino

-ClickHouse

-Apache Iceberg

-BigQuery

-MotherDuck

About the Role

We are looking for a Backend / Data Infrastructure Engineer to work on a platform for data processing and analytics.

The main focus of the role is distributed systems, database engines, data infrastructure, and data performance optimization.

You will work on core product technologies, query engines, storage layers, and systems that enable efficient work with different data sources and compute environments.

Key Responsibilities

-Develop and maintain query engine APIs for modeling and testing query engine behavior.

-Build and improve data workflows and integrations with different compute engines.

-Add support for new systems, including BigQuery, Trino, MotherDuck, and others.

-Work on a storage layer optimized for Apache Iceberg and analytical workloads.

-Develop mechanisms for ensuring data integrity across multiple query engines.

-Build logic for automatically routing queries between Snowflake, DuckDB, Trino, and ClickHouse based on performance, cost, and data freshness.

Work with logical query plans and their serialization.

Build automated benchmarking systems to compare:

-query latency;

-throughput;

-cost;

-performance across real-world workloads.

Requirements

-Experience in Data Engineering, Data Infrastructure, or Backend Engineering.

-Practical experience with distributed systems.

-Experience working with database engines / data warehouses.

-Understanding of ETL and data modeling.

-Experience with database optimization and performance tuning.

-Experience with Python and Rust.

-Understanding of query engine architecture is a strong plus.

-Experience with Snowflake, Databricks, Dremio, ClickHouse, or other cloud data platforms.

-Experience with Iceberg, Hudi, or Delta Lake is a plus.

-Experience working in early-stage startups is a plus.

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:

Our client is a major online gaming operator building a next-generation transactional Player Account Management platform from scratch (greenfield). Operating in a highly regulated, high-volume environment with millions of concurrent users, the platform processes real-time financial transactions requiring sub-second performance, strict data integrity, and ultra-high availability during extreme traffic spikes.

You will own the multi-layer lakehouse architecture, CDC streaming pipelines, and data storage design, directly shaping the scalability, reliability, and governance of the core data platform.

Key Responsibilities:

• Design and maintain a multi-layer lakehouse using Iceberg or Delta Lake, including schema evolution and query performance optimization.
• Build real-time CDC ingestion pipelines with Kafka and Debezium, handling late or duplicate events.
• Ensure data freshness, quality, and automated reconciliation with source transactional systems.
• Implement data governance practices, including metadata, lineage, PII protection, and audit trails.
• Develop production data pipelines using Python, Airflow, or Dagster.

Key Requirements:

• 5+ years of data engineering experience with large-scale data systems, data lakes, or lakehouse environments.
• Production experience with AWS S3 and analytics platforms such as Snowflake, Databricks, or BigQuery.
• Hands-on experience with open table formats like Iceberg, Delta, or Hudi.
• Experience building CDC and streaming data pipelines using Kafka or Debezium.
• Strong SQL, Python, and data modeling skills for relational and transactional data.
• Background in financial, transactional, or audit-heavy domains is a plus.
   

What We Offer:

• 20 paid vacation days plus official national holidays.
• Full legal and accounting support.
• Fully remote work model within Poland with a powerful workstation provided or co-working space support.

Join Acropolium - Where People Matter, and Ideas Grow!

Acropolium is an international software development and tech consulting company with over 22 years of experience in delivering scalable, human-centric digital solutions. We partner with clients across 20+ industries - from bold startups to established unicorns - helping them grow, adapt, and thrive.
With ISO 9001 certification and 450+ successful applications behind us, we combine deep technical expertise with a people-first culture. Our core team is in it for the long run - over 60% have been with us for more than 10 years - and we’re growing fast.

About the Role
We are looking for a Senior Data Engineer to join a long-term project.
You’ll work on designing and developing scalable data solutions using modern Databricks and Azure technologies, combining hands-on engineering with architectural responsibilities.

Key Responsibilities

• Design and evolve scalable data architectures based on Data Lakehouse principles.
• Define technical solutions and architectural approaches for data processing, integration, and storage.
• Design, develop, and optimize data pipelines using Databricks, Apache Spark, and Python.
• Build and maintain data workflows and integrations using Azure Data Factory and Azure Storage.
• Establish and maintain data governance, access control, and data management practices using Unity Catalog.
• Ensure data solutions are scalable, reliable, secure, and maintainable.
• Collaborate with engineers and other stakeholders to translate business and technical requirements into effective data solutions.
• Contribute to technical decisions, architecture documentation, and best practices across the project.
• Identify opportunities to improve existing data solutions, processes, and overall platform performance.
   

What We’re Looking For

• Senior-level experience in Data Engineering, with strong architectural thinking
• Hands-on experience with Databricks, Spark, and Python
• Solid understanding of Data Lakehouse architecture
• Practical experience with Unity Catalog, Azure Data Factory, and Azure Storage
• Ability to design scalable and reliable data solutions and make technical decisions
• English - B2 or higher
• German B1 will be a plus
   

Why You’ll Love Acropolium
 

• People-First Culture - A team that values openness, trust, and genuine connections.
• Keep Growing - Career development, learning resources, and opportunities to expand your expertise.
• Work-Life Balance - A culture that values your personal time and well-being.
• Work Equipment Support - We help you get the equipment you need to work comfortably and effectively.
• Rewards & Recognition - Earn internal Kudos currency and redeem it for Acropolium merch.
• Referral Program - Recommend great people to join our team and get rewarded.
• Little Extras, Big Smiles - Enjoy company events and thoughtful annual gifts for our team members and their children.
• Making a Difference Together - We support our colleagues serving in the military and believe in making a positive impact beyond our business.
   

Let’s DARE TO CREATE together.
Join Acropolium - not just a job, but a place to belong.

Domain: Banking & Finance
Experience: 5+ years
Employment Type: Full-time
Location: Hybrid — Kraków, Poland
Project Duration: November 2, 2026 – April 30, 2027

About the Project

We are looking for Data Engineer to join a modern engineering environment and work on reliable, scalable data solutions.

The role focuses on building data pipelines and processing frameworks, integrating multiple platforms and systems, and making data accessible, governed, and fit for purpose. The team works with Scala, Java, Python, Apache Airflow, and cloud technologies.

Experience with data fabrics, cloud platforms, and Apache Airflow will be particularly valuable.

Responsibilities

• Design, develop, and maintain robust data pipelines and engineering frameworks
• Build scalable data processing solutions using Scala, Java, and Python
• Integrate data platforms, applications, APIs, and other technology components
• Develop and support workflows using Apache Airflow
• Work with cloud-based data services and infrastructure
• Contribute to data fabric architectures, including data integration, discovery, access, and governance
• Develop and consume APIs to support data movement and system interoperability
• Apply software engineering best practices, including version control, testing, code reviews, documentation, and CI/CD
• Monitor, troubleshoot, and improve the performance, resilience, and quality of data solutions
• Collaborate with data architects, software engineers, platform teams, analysts, and business stakeholders
• Support security, data protection, and operational controls throughout the engineering lifecycle

Requirements

• Professional experience in data engineering, software engineering, or a closely related field
• Strong programming skills in Scala, Java, and Python
• Strong knowledge of SQL / DML
• Experience designing and implementing data pipelines, processing frameworks, or integration services
• Experience integrating multiple tools, platforms, and systems
• Hands-on experience with Apache Airflow, including DAG development, scheduling, monitoring, and troubleshooting
• Understanding of REST APIs, authentication, and error handling
• Experience working with cloud platforms and cloud-native engineering practices
• Familiarity with data architecture, data integration patterns, and data quality
• Experience with Git, automated testing, and CI/CD
• Ability to investigate complex technical issues and communicate solutions clearly
• Degree, equivalent qualification, or demonstrable professional experience in computer science, engineering, data, mathematics, or a related field

Nice to Have

• Experience with Google Cloud Platform (GCP), including BigQuery, Cloud Storage, Pub/Sub, Dataflow, or Dataproc
• Experience with data fabric architectures, metadata, data catalogues, lineage, or federated data access
• Familiarity with Apache Spark and other distributed processing technologies
• Experience with containers, orchestration, or Infrastructure as Code
• Knowledge of security, access control, encryption, and regulatory requirements in data environments
• Experience working in Agile, product-oriented, or DevOps environments
• Relevant cloud, data engineering, or software engineering certifications

Soft Skills

• Strong ownership and accountability for deliverables
• Ability to collaborate effectively across engineering, architecture, operations, and business teams
• Ability to balance delivery speed with maintainability, resilience, and control
• Structured approach to problem-solving and continuous improvement
• Ability to communicate technical concepts clearly to both technical and non-technical stakeholders
• Collaborative mindset and appreciation of diverse perspectives

Tech Stack

Scala, Python, Java, SQL, Apache Airflow, Apache Spark, REST APIs, Git, CI/CD, GCP, Data Integration, Data Fabric

Project Highlights:

• Duration: Long-term stability (3+ years).
• Work Setup: 100% Remote.
• Engagement Model: Project-based employment / contractual setup (specific employment model and local arrangements to be discussed).

Key Requirements:

• Core Tech: Strong hands-on expertise with Python and PySpark.
• Cloud & Data Stack: Proven experience with Azure Databricks and Azure Data Lake (ADLS).
• Data Engineering: Deep background in designing ETL/ELT processes, building Data Lakehouse architectures, and optimizing data pipelines.
• Languages: Fluent English (Upper-Intermediate or higher).

Nice to Have:

• Experience with Azure Data Factory and Unity Catalog.
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What We Offer:

• Work on complex, high-impact engineering challenges in Big Data.
• Long-term stability without the hassle of jumping between short gig assignments.
• Fully remote flexibility.