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Want to expand your Data Engineering development team in Poland? Here are some of our top remote senior Data Engineering developers in Poland for you to browse, interview, and hire.
I am Software/Data Engineer. Previously I was working on backend of distributed systems, big data processing systems. Have deep computer science knowledge, functional programming skills. I am able to learn fast and dig deep. Have commercial experience in technologies: \- Scala/Java/Python/Groovy/SQL \- Docker, Kubernetes, Jenkins \- Spring Boot, Cats Effect \- PostgresSQL, MySQL, Oracle, Apache Hive \- Apache Hadoop, Apache Spark, Apache Kafka, Apache Airflow \- AWS: CloudFormation, EC2, Kinesis, S3, EKS Things that are interesting for me: \- System programming: database engines, search engines, programming language compilers, load balancers. \- Distributed systems: consensus, big data processing engines, message queues. \- High-performance computing. \- C++/Rust/Haskell/OCaml
Lead Data Engineer with demonstrated experience, serving Silicon Valley start-up and corporation clients. Specialized in backend development, event, and batch processing, essential for scaling foundational systems and data pipelines. Expert in Python, SQL, and prominent data engineering technologies like Airflow, AWS, Databricks and Snowflake. Proven proficiency in data systems architecture, designing distributed systems, data modelling and ETL processes. Recognized for leadership in technical mentorship, enhancing team growth, and streamlining data pipeline construction. Fluent in English and Polish, with a strong educational background in computer science from Warsaw University of Technology, and a good grasp of basic accounting principles.
A data engineer with 3+ years of experience in the industry, ELT/ETL pipeline building, Microsoft certified Azure Data Engineer, able to maintain and build scalable data pipelines, experienced in OOP data structures and algorithms, Snowflake, DBT, Azure, AWS, relational & non-relational DB, real-time streaming, batch processing, work with data warehouses, data lakes, and easily work with analytical tools that can work with messy data and make it easily usable for data scientist and analysts.
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