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Tier-1 brand, mid-level generalist data role, metro location drive high applicant competition.
Core data engineering skills (Databricks, Spark, AWS) are broadly transferable across industries.
Explicit 4–6 years requirement and mandatory Databricks/AWS/Spark/Python skills make filters strict.
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Design, build, and maintain scalable ETL processes and data pipelines using Databricks on AWS and Spark for the Sisal team.
Develop and optimize Lakehouse architectures for efficient processing of large-scale datasets with a focus on data availability, quality, and reliability.
Drive automation using CI/CD pipelines, Infrastructure as Code (IaC), and DevOps practices to enhance database performance and data governance.
4 to 6 years of experience in data engineering and ETL pipeline development.
Hands-on experience with Databricks on AWS, Spark streaming, and AWS data services including DynamoDB, Glue, Athena, EMR, Redshift, Lambda, and Kinesis.
Proficiency in Python (PySpark/Spark SQL preferred) and Java programming languages.
Work Experience Required: 4 to 6 years
Experienced in building scalable data warehousing and big data solutions in cloud environments, particularly on AWS.
Skilled in implementing modern data platform technologies including Lakehouse architectures and streaming data processing.
Familiar with CI/CD, Agile methodologies, and DevOps best practices to deliver efficient and reliable data solutions.