Senior Data Engineer @

2 days ago

Kraków, Lesser Poland, Czech Republic ITDS Full time
You're ideal for this role if you have:
  • Strong experience in PySpark, Scala, or similar data engineering languages 
  • Hands-on experience building production data pipelines using Hadoop, Spark, and Hive 
  • Knowledge of cloud platforms and migrating on-premise solutions to the cloud 
  • Experience with scheduling tools such as Airflow and workflow orchestration 
  • Strong SQL skills and experience with data modelling and warehousing principles 
  • Familiarity with Unix/Linux platforms and big data distributed systems 
  • Experience with version control tools such as Git and CI/CD pipelines (Jenkins, GitHub Actions) 
  • Understanding of ETL/ELT frameworks and data formats (Parquet, ORC, Avro) 
  • Proven ability to troubleshoot, debug, and optimize data processing workflows 
  • Experience working in Agile environments and collaborating across global teams 
It is a strong plus if you have:
  • Experience with near real-time event streaming tools (Kafka, Spark Streaming, Apache Flink) 
  • Exposure to MLOps or running machine learning models in production 
  • Knowledge of DevOps practices, containerization, and cloud design patterns 
  • Experience developing in Java or other programming languages 
  • Familiarity with Elasticsearch and ingestion pipelines 

Join us, and engineer robust systems for millions of users

Kraków - based opportunity with hybrid work model (6 days/month in the office).

As a Senior Data Engineer, you will be working for our client, a global digital-first bank focused on delivering innovative financial solutions at scale. You will join a dynamic engineering team responsible for building and enhancing data solutions that support critical business applications used by millions of customers. Your role will involve developing robust and fault-tolerant data pipelines, automating processes, and supporting cloud and on-premise deployments. You will collaborate with engineers, data analysts, and business stakeholders to ensure data solutions are efficient, scalable, and aligned with the bank's digital and data transformation initiatives.

,[Designing, developing, and maintaining end-to-end data pipelines across cloud and on-premise systems , Implementing robust ETL/ELT processes using PySpark, Hadoop, Hive, and Spark SQL , Collaborating with engineers and analysts to translate requirements into scalable data solutions , Automating workflows and optimizing data engineering processes for efficiency and reliability , Ensuring data quality, accuracy, and consistency across pipelines and applications , Migrating on-premise data solutions to cloud platforms such as GCP, AWS, or Azure , Participating in code reviews, promoting development standards, and sharing knowledge with peers , Supporting production environments, troubleshooting issues, and monitoring performance and scale , Contributing to system architecture, design discussions, and Agile development processes ] Requirements: Data pipelines, Cloud, ETL, PySpark, Hadoop, Hive, Spark SQL, Data engineering, Cloud platform, GCP, AWS, Azure, Scala, Spark, Airflow, SQL, Data modelling, Unix, Linux, Git, CI/CD Pipelines, GitHub Actions, Spark Streaming, Flink, MLOps, Machine learning, DevOps, Design Patterns, Java, Elasticsearch, Kafka Additionally: Sport subscription, Private healthcare.

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