Analyse and migrate existing workflow integrations to new platforms.
Develop workflow orchestration, including triggers, routing, retries and exception handling.
Build automation for batching, pagination, looping and throttling.
Design and maintain ETL pipelines for file-based data ingestion into relational databases.
Perform schema validation, data quality checks and error reconciliation.
Develop data pipelines using cloud-based big data platforms and distributed processing frameworks such as Spark.
Support Lakehouse-based data environments for batch and streaming workloads.
Implement logging, monitoring and alerting for data pipelines and workflows.
Troubleshoot issues and participate in root-cause analysis and continuous improvement.
Work with application teams to understand data requirements and upstream/downstream dependencies.
Ensure data pipelines meet performance, reliability, security and governance requirements.

3-5 years of relevant experience in Data Engineering, Integration Engineering or a related role.
Experience with workflow orchestration or integration platforms.
Hands-on experience with ETL and file-based data ingestion.
Proficient in SQL and relational databases such as SQL Server.
Experience with Spark or other distributed data processing frameworks.
Experience with Python or other programming/scripting languages.
Understanding of batch and streaming data processing.
Experience with cloud platforms and managed data services.
Experience working with REST APIs, JSON and CSV.
Understanding of CI/CD for data and integration workflows.
Good understanding of data quality, error handling and pipeline reliability.

Experience with Lakehouse table formats, incremental processing and time travel.
Experience with Flink or Spark Streaming.
Familiarity with analytical query engines.
Experience in banking, financial services, manufacturing or other regulated environments.
Knowledge of data observability and data quality tools.

Data Engineer AMK