Data Architecture Engineering - Senior

Meet our Team 

The Enterprise Data Analytics team is responsible for collecting information from a variety of sources, storing it in enterprise datalake, and ensuring its security. The team also manages data segregation, organizing and categorizing the data to facilitate easy access and usability. This structured approach enables the team to deliver valuable insights and information to internal clients, supporting a range of applications, reporting dashboards and Machine Learning teams.

Comprising 15 members, this international team is spread across different geographical locations, including Poland, India, the USA, and Mexico. This diverse composition brings together a wealth of cultural perspectives and technical expertise, enhancing the team's ability to innovate and respond to global challenges in data analytics.

The Role

The ideal candidate will be tasked with ingesting data from multiple sources into our Data Lake, utilizing ETL tools, API integrations, and Python scripts. This role involves supporting and enhancing existing data pipelines, ensuring compliance with coding standards, and maintaining comprehensive documentation. The candidate will also be responsible for monitoring production processes and swiftly addressing any issues that arise. Close collaboration with DevOps and Data teams is essential to ensure seamless data integration and system performance.

We welcome applications from remote candidates based in Poland, with the expectation of working within Polish time zones.

Requirements

• 4 to 7 years of hands-on experience in Data Engineering or a related role
• Proficiency in one or more programming languages such as Python
• Hands-on experience with SQL
• Strong experience with ETL processes and tools (e.g., Pentaho, SSIS, Informatica, HVR)
• Experience with AWS cloud services
• Experience with data pipeline automation and orchestration tools (e.g., Airflow)
• Proficiency with GIT and CI/CD practices
• Experience with monitoring and logging solutions
• Familiarity with modern data development practices (e.g., data lakes, real-time streaming)

Technology Set: Linux, AWS S3, AWS Lambda, AWS Glue, Athena, Redshift, EC2, Airflow, Spark.

Concepts: Database, Data Warehouse, Big Data, Data Governance, Data Security