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.
• Collaborate with analysts engineers and business leads to translate raw multi-source data into governed insight-ready datasets that support performance reporting and downstream decisioning.
• Contribute to data quality standards monitoring and the platform roadmap defining best practices for reusable components transformation patterns and observability that scale across teams and clients.
• Prepare certified well-modeled datasets for the consumption layer and reporting tools such as Power BI ensuring metrics are consistent governed and traceable to source.

Qualifications

• 4-6 years of experience as a Data Engineer or in a similar role building and operating scalable production data pipelines.
• Bachelors Degree in Computer Science Engineering Information Systems or a related field required; Graduate degree preferred.
• Hands-on experience with Adverity for data onboarding and normalization including building and maintaining Data Refinery ingestion pipelines across multiple marketing and media sources.
• Proven experience managing data transformations in Trifacta (Alteryx Designer Cloud) including building governed repeatable wrangling recipes for cleansing and standardization.
• Advanced expertise with Databricks including Unity Catalog governance (catalogs permissions lineage metadata) transformation development in SQL and PySpark and Databricks Workflows Notebooks and Jobs.
• Demonstrated experience designing and operating data quality health checks including integrity tests reconciliation logic schema validation and automated monitoring within a Databricks environment.
• Familiarity with dbt (dbt Labs) for transformation orchestration and testing and with GitHub for version control CI/CD and code governance in a collaborative development workflow.
• Strong proficiency in SQL and Python for data engineering transformation and automation.
• Working knowledge of medallion (bronze/silver/gold) architecture data modeling lineage and governance best practices for analytics-ready data.
• Self-starter with the ability to learn new tools quickly and deliver scalable well-documented solutions across the data stack driving continuous improvement and measurable impact.
• Bonus: Experience in advertising marketing or digital media environments particularly performance reporting reconciliation automation or data quality optimization.

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