Data Engineer - Python/SQL/ETL

About the Role :

We're seeking a Data Engineer with 3 - 4 years of experience to join our growing tech team. The ideal candidate will have hands-on experience in building and managing scalable data systems on AWS using Spark and modern data frameworks.

Key Responsibilities :

• Design, build, and maintain end-to-end data pipelines for ingestion, transformation, and delivery of high-volume data.
• Develop Spark-based ETL/ELT workflows for both batch and real-time streaming data.
• Integrate data from multiple internal and external systems using Kafka, Kinesis, or other streaming frameworks.
• Build and manage data models, warehouses, and lakehouses using AWS services such as S3, Glue, Redshift, Athena, etc.
• Implement data quality checks, validation rules, and monitoring to ensure reliability and consistency.
• Collaborate with data analysts and scientists to provide clean, structured datasets optimized for analytics and ML.
• Work with orchestration tools (Airflow, MWAA, Step Functions, etc.) for automated workflow scheduling.
• Continuously optimize data pipelines for cost, scalability, and performance.

Tech Stack :

• Spark, AWS (S3, Glue, Redshift, Kinesis), Kafka, Airflow, Python, SQL.

Required Skills :

• Strong programming skills in Python for data manipulation and automation.
• Hands-on expertise in Apache Spark (PySpark or Spark SQL) for large-scale data processing.
• Deep understanding of AWS data ecosystem - S3, Glue, Lambda, Redshift, Athena, EMR, Kinesis, IAM.
• Experience with real-time streaming platforms such as Kafka, Kinesis, or Flink.
• Strong command of SQL and data modeling (star schema, dimensional modeling, partitioning).
• Proficiency with data orchestration and workflow management tools (Airflow, Step Functions, etc.).
• Familiarity with Git, CI/CD, and modern development best practices.
• Experience working in Linux/Unix environments and handling large datasets efficiently.

Good to Have :

• Exposure to data lakehouse technologies (Delta Lake, Iceberg, Hudi).
• Understanding of data governance, cataloging, and lineage tools (Glue Data Catalog, Amundsen, DataHub).
• Familiarity with containerization and deployment (Docker, ECS, EKS).
• Basic understanding of AI/ML.

Qualifications :

• Bachelor's degree in Computer Science, IT, Engineering, or related technical field.
• 3 - 4 years of experience in data engineering, big data, or analytics infrastructure.