- Design, develop, and maintain scalable data pipelines for batch and streaming workflows.

- Implement robust ETL/ELT processes to extract data from various sources and load into data warehouses.

- Build and optimize database schemas following best practices in normalization and indexing.

- Create and maintain documentation for data flows, pipelines, and processes.

- Collaborate with cross-functional teams to translate business requirements into technical solutions.

- Monitor and troubleshoot data pipelines to ensure optimal performance.

- Implement data quality checks and validation processes.

- Build and maintain CI/CD workflows for data engineering projects.

- Stay current with emerging technologies and recommend improvements to existing systems.

Requirements :

- Bachelor's degree in Computer Science, Information Technology, or related field.

- Minimum 4+ years of experience in data engineering roles.

- Strong proficiency in Python programming and SQL query writing.

- Hands-on experience with relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra).

- Experience with data warehousing technologies (e.g., Snowflake, Redshift, BigQuery).

- Proven track record in building efficient and scalable data pipelines.

- Practical knowledge of batch and streaming data processing approaches.

- Experience implementing data validation, quality checks, and error handling mechanisms.

- Working experience with cloud platforms, particularly AWS (S3, EMR, Glue, Lambda, Redshift) and/or Azure (Data Factory, Databricks, HDInsight).

- Understanding of different data architectures including data lakes, data warehouses, and data mesh.

- Demonstrated ability to debug complex data flows and optimize underperforming pipelines.

- Strong documentation skills and ability to communicate technical concepts effectively.