Mid/Senior Data Engineer

Mid/senior data engineer - capco poland

We offer a flexible collaboration model based on a B2B contract, with the opportunity to work on innovative AI and automation initiatives for leading financial institutions.

At Capco Poland, we’re not just another consultancy - we’re the spark behind digital transformation in the financial world. As a global leader in technology and management consulting, we help our clients tackle complex challenges across banking, payments, capital markets, wealth, and asset management.

Our secret? A culture that’s fast, flexible, and fiercely entrepreneurial. We move quickly, think creatively, and always put our people first.

We’re passionate about growth - both for our clients and ourselves - and that means attracting talented professionals who want to develop their skills, take ownership, and make a real impact.

We’re proud to be

• Trailblazers in banking, payments, capital markets, wealth, and asset management
• Champions of an agile, nimble, and innovative work environment
• Dedicated to building a team of talented professionals who share our drive and vision

The role

We are looking for a Mid Data Engineer to join our growing data engineering team and contribute to building scalable, reliable data solutions for our financial services clients.

You will work with modern data technologies and cloud platforms, developing and maintaining data pipelines, processing large datasets, and supporting the delivery of enterprise-scale data solutions.

This is a great opportunity for a Data Engineer who already has hands-on commercial experience and wants to further develop their expertise in Python, Apache Spark, Hadoop, Linux, and Google Cloud Platform (GCP) while working on complex international projects.

What you’ll do

• Design, develop, and maintain scalable data pipelines and data processing solutions.
• Develop data transformation and processing workflows using Python and Apache Spark.
• Work with large-scale datasets in distributed environments using Hadoop and related technologies.
• Build and support cloud-based data solutions on Google Cloud Platform (GCP).
• Develop reliable ingestion processes integrating data from multiple source systems.
• Implement data transformations, validation rules, and data quality checks.
• Troubleshoot data pipeline issues and support performance optimization.
• Work with Linux-based environments, including scripting, deployment, and operational activities.
• Collaborate with Data Engineers, Architects, Analysts, and other project stakeholders to translate business requirements into technical solutions.
• Participate in code reviews and follow software engineering and data engineering best practices.
• Create and maintain technical documentation covering data flows, dependencies, configurations, and operational procedures.
• Support deployment, testing, stabilization, and ongoing maintenance of data solutions.

What we’re looking for

• 2-4+ years of commercial experience in Data Engineering or a similar role.
• Good hands-on programming skills in Python.
• Practical experience with Apache Spark, including building and maintaining data processing jobs.
• Experience working with Hadoop or distributed data processing ecosystems.
• Good knowledge of Linux and command-line environments.
• Commercial experience with Google Cloud Platform (GCP) and relevant data services.
• Good understanding of ETL/ELT processes, data pipelines, and data transformation concepts.
• Working knowledge of SQL and relational data concepts.
• Understanding of data quality, monitoring, and troubleshooting practices.
• Familiarity with Git and modern software development practices.
• Ability to work effectively in an Agile environment and collaborate with distributed teams.
• Good communication skills and English at a minimum B2 level.

Nice to have

• Experience with GCP services such as BigQuery, Cloud Storage, Dataproc, Dataflow, or Pub/Sub.
• Experience in Financial/Banking domain
• Experience with orchestration tools such as Apache Airflow.
• Familiarity with CI/CD processes for data solutions.
• Knowledge of data modelling and data warehouse concepts.
• Experience working with financial services or banking clients.
• Familiarity with containerization technologies such as Docker or Kubernetes.

Online recruitment process

• Screening call with the Recruiter
• Hiring Manager Technical Interview
• Client Interview
• Feedback / Offer