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.