ETL Data - Senior Engineer
Iris Software (company site, opens in a new tab)NoidaOn-siteDirect career pageEst. 18–30 LPA
Posted on 9 Sept
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Job description
Structured overview of role & requirements
About This Role
- Design and implement scalable batch and streaming data pipelines using PySpark, Databricks, and related distributed processing frameworks.
- Architect and optimize data ingestion, transformation, and processing solutions on platforms like Snowflake and Delta Lake to support enterprise-scale data.
- Lead data governance, quality assurance, workflow orchestration (Apache Airflow or Databricks Workflows), and operational monitoring for reliable data platform delivery.
Minimum Requirements
- Proficiency with PySpark, Databricks Workflows, Delta Lake on Databricks, and Amazon Kinesis.
- Experience with data engineering on cloud platforms, specifically AWS services including AWS SNS, SQS, Kinesis, Glue, EMR, Redshift, and associated security/monitoring tools.
- Strong skills in SQL, Python, and Unix/Linux shell scripting relevant to ETL and data integration tasks.
- Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Experienced in designing end-to-end data engineering solutions aligned with business analytical objectives and modern Lakehouse architecture principles.
- Comfortable driving data quality, validation, and governance in complex, distributed data ecosystems with high operational visibility demands.
- Skilled in leveraging AI-assisted engineering techniques to enhance productivity, testing, and documentation within data engineering workflows.