Data Engineering, AI Adoption and Productivity

Job Description We are seeking a Data Science Senior Associate focused on building and operating resilient datasets, pipelines, and reusable metrics that support hypothesis-driven analyses and experiments across the product development lifecycle (PDLC) In this role, you will be hands-on in designing, developing, and maintaining data products that are reliable, observable, and well-documented—enabling partners across product, engineering, and analytics to measure what’s driving value, where friction exists, and how operating-model changes impact outcomes as teams adopt more agentic ways of working. You’ll contribute to engineering standards and help raise the quality bar through strong delivery and collaboration. Job Responsibilities Build and operate scalable batch/streaming pipelines with SLAs, monitoring, and incident response participation (as needed). Create and maintain trusted data products (dimensions, event models, marts) with clear ownership and documentation. Deliver metrics and feature-ready datasets for AI adoption/productivity measurement; manage definition changes over time. Implement data quality and governance controls (validation, reconciliation, lineage, access, retention, auditability). Orchestrate workflows in Airflow (or equivalent), including backfills and retries. Model/transform data using SQL and dbt (or equivalent) for trusted reporting and repeatable measurement. Write production-grade Python/PySpark with testing, performance tuning, and maintainable design. Partner with cross-functional stakeholders to define requirements, success criteria, and metric interpretation across finance, PDLC/SDLC, and AI tool logs. Contribute to engineering best practices (version control, code review, CI/CD, runbooks) and improve observability and cost/performance. Mentor peers through reviews, documentation, and knowledge sharing (no formal people management). Required Qualifications Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience. 3+ years building production data solutions; strong ownership and delivery. Strong engineering fundamentals (OOP, testing, development lifecycle). Strong data modeling skills (dimensional, normalized, event-based). Experience with Databricks and/or Spark/PySpark. Strong SQL; experience with dbt (or equivalent) and building testable data codebases. Experience operating orchestration pipelines (Airflow or equivalent). Proven ability to build and maintain reliable metrics as sources/definitions evolve. Effective delivery in ambiguous, multi-stakeholder environments. Preferred Qualifications Experience with modern lakehouse/warehouse patterns and broader cloud data platforms (e.g., Databricks, Snowflake). Experience with BI/semantic layers and metrics management practices. Exposure to experimentation or hypothesis-driven analytics approaches (e.g., measurement design to support tests, rollouts, and pre/post evaluation); deep causal specialization not required. Experience improving observability (data freshness/SLA monitoring, lineage, alerting) and contributing to operational maturity (runbooks, incident follow-ups).