Sr. Associate

Job Description: Data Engineer (Manager and/or Senior) - Job Description (L30) As the “Data Engineer (Manager and/or Senior)”, reporting to the “Senior Director Data Engineer”, you will own the end-to-end data supply chain that powers analytics and AI for our global clients. You will onboard multi-source data through Adverity into the Data Refinery layer, manage data transformations in Trifacta, and land governed, quality-checked datasets in Databricks, using Unity Catalog for governance and lineage, SQL and PySpark for further transformation, and automated data quality health checks to embed trust at every stage. You will apply dbt and GitHub for orchestration, version control, and repeatable, well-governed delivery. Working closely with Analytics, Media, Product, and Engineering teams, you will turn fragmented, reactive data processes into a proactive, well-documented, AI-ready foundation built on a medallion (bronze/silver/gold) architecture. You are ideally based in the Greater Detroit, MI area, but we are considering candidates within the continental United States as well. Responsibilities Onboard and normalize multi-source data through Adverity, standing up and maintaining Data Refinery pipelines that connect marketing, media, and platform sources into a single, reliable ingestion layer. Ingest, model, and reconcile DSP data (e.g., DV360, The Trade Desk, Amazon DSP) against CM360 ad-server delivery, applying programmatic media expertise to distinguish buy-side activation and bidding from independent ad serving/counting, and to resolve impression, spend, and attribution discrepancies between platforms. Manage and maintain data transformations in Trifacta, building governed, repeatable wrangling recipes that cleanse, standardize, and shape raw inputs into analytics-ready structures. Govern data assets in Databricks Unity Catalog, managing catalogs, schemas, permissions, lineage, and metadata to keep data secure, consistent, and discoverable across the platform. Develop and optimize further transformations in Databricks using SQL, PySpark, notebooks, and workflows, extending refined data through the medallion (bronze/silver/gold) architecture into curated, consumption-ready models. Design, run, and monitor data quality health checks in Databricks, including row-level integrity, schema and version validation, reconciliation logic, and automated error logging, to embed trust and observability at every stage of the pipeline. Working understanding and familiarity to apply modular, tested, and documented transformation orchestration, and GitHub for version control, CI/CD, code review, and change governance across SQL, Python, and YAML assets. Design modular, reusable, and well-documented data models that support analytics, reporting, and AI enablement, with clear definitions, lineage, and business context.