Data Engineer - MLOPS & Flink

Lead the design and development of scalable feature platforms and ML pipelines. Own MLOps practices, contribute to platform architecture, and mentor engineers while incorporating key data engineering best practices. The person is expected to do hand's on work as well.

Key Responsibilities

• Design and implement scalable, reusable feature pipelines (batch and real-time)

• Develop advanced feature transformations and complex data models

• Optimize performance, latency, and cost efficiency

• Ensure feature quality, validation, and SLAs

• Work closely with data engineering teams on upstream data pipelines and ingestion design

• Contribute to feature store and data platform architecture

• Collaborate across Data Science, MLOps, and Platform teams

• Lead production deployment, monitoring, and incident resolution

• Mentor junior engineers and drive engineering best practices

• Translate business use cases into scalable feature logic

Must-Have Skills

• Advanced Python and SQL

• Strong experience with distributed processing (Spark / Flink)

• Deep expertise in feature engineering patterns

• Strong understanding of data pipelines and ETL architecture

• Experience with feature stores

• Strong understanding of ML lifecycle and model optimization

• Experience with CI/CD, monitoring, and production systems

• Cloud platform experience (Azure / AWS / Google Cloud Platform)

• Experience in data quality, validation, and drift detection