Sr/Jr Feature engineer

For Junior:

Build and maintain scalable feature pipelines that power machine learning models. The role is primarily MLOps and feature engineering focused, with exposure to core data engineering concepts such as data ingestion and transformation.

Key Responsibilities

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

• Develop feature transformations and data processing logic

• Ensure feature quality, validation, and SLAs (freshness, accuracy, reliability)

• Work with upstream data pipelines and support data ingestion needs where required

• Monitor and optimize pipeline performance, latency, and cost

• Collaborate with Data Science and ML Engineering teams

• Support deployment, monitoring, and issue resolution

• Follow feature store and platform best practices

Must-Have Skills

• Strong Python, SQL

• Experience with Spark / Flink or similar distributed processing

• Understanding of feature engineering and transformations

• Understanding of data pipelines and ETL concepts

• Exposure to cloud platforms (Azure / AWS / GCP). Experience with feature stores (Feast, Hopsworks, SageMaker)

• Knowledge of data quality and validation

• Familiarity with CI/CD and testing practices

Good to Have

• Understanding of ML lifecycle

• Exposure to monitoring and observability tools

• Basic performance tuning experience

Experience

• 3–5 years in Feature Engineering, Data Engineering, or ML Engineering

For Senior:

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 / GCP)

• Experience in data quality, validation, and drift detection

Good to Have

• Experience building enterprise feature or ML platforms

• Advanced performance tuning and cost optimization

• Exposure to broader data platform architecture

• Strong stakeholder communication and leadership experience

Experience

• 6–10+ years with mentoring/technical leadership experience