Job Description 2

DS/Feature Engineer Feature Development:

• Design, build, and maintain curated features for ML models and analytics products.
• Partner with data scientists, marketing team members, and advanced analytics to translate modeling needs into feature specifications and acceptance criteria.

Feature Pipelines & Performance:

• Implement scalable feature pipelines (batch/stream) using Databricks, pyspark, and spark SQL.
• Optimize feature computation for cost and latency; ensure incremental processing and backfills are reliable.

Data Quality, Lineage & Documentation:

• Build automated checks for schema changes, missingness, outliers, and drift.
• Maintain feature definitions, lineage, and usage documentation to enable reuse and governance.

Operationalization:

• Work with ML Ops to publish features to a feature store and enable consistent training/serving.
• Support troubleshooting and root-cause analysis for feature-related incidents.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.

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Insight Global's Workforce Privacy Policy:

https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements

• 3-6+ years of experience in analytics engineering, data engineering, or ML feature engineering.
• Strong SQL and Python; experience with Spark/Databricks preferred.
• Experience designing data models (dimensional, lakehouse) and creating reusable transformations.
• Experience with data quality testing and documentation practices.
• Understanding of ML workflows (training vs.

inference consistency, leakage risks).