p>Own the end-to-end delivery of a portfolio of fixed-price client engagements, ensuring outcomes are achieved on scope, on time, and within budget Maintain rigorous visibility into project health across the team tracking effort burn, milestone progress, and risk indicators on every active engagement Lead scope management with discipline: identify scope creep early, facilitate change order conversations with clients, and protect project margin without damaging relationships Ensure project financials are accurate and current own gross margin reporting for your teams delivery portfolio in partnership with the Director Conduct structured post-engagement reviews to capture lessons learned and feed improvements back into scoping, methodology, and estimation practices. Strong working knowledge of modern cloud data platforms (e.g., Databricks, Microsoft Fabric, BigQuery, etc.) and the patterns used to build production data solutions on them Familiarity with AI and ML engineering including model deployment, MLOps, and practical application of LLMs (RAG pipelines, prompt engineering, API integration) Proficiency in Python and SQL; hands-on familiarity with dbt, Airflow, Spark, or equivalent transformation and orchestration tooling Ability to read, review, and provide meaningful technical feedback on engineering work you may not be coding day-to-day, but you can tell good from poor solution design Understanding of DataOps principles: CI/CD for data, data quality frameworks, testing, and observability in production.