Role Accountabilities and Responsibilities
Build reusable, metadata-driven ingestion frameworks and batch/streaming ELT pipelines that promote data through Bronze, Silver, and Gold with defined quality gates, incremental/CDC processing, and restartable, idempotent design
Design dimensional, normalized, and data vault models, and build curated Gold data products with conformed dimensions that reconcile across regions and source systems
Profile sources, implement automated data quality and reconciliation controls, and support master and reference data management
Maintain the semantic layer, catalog, lineage, and business glossary with data stewards; extend classification, masking, and access controls to AI use cases
Develop on Databricks (PySpark, Spark SQL, Delta Lake, Delta Live Tables, Workflows, Unity Catalog), optimizing performance and cost and contributing to disaster recovery planning
Write tested, production-grade Python and SQL using Git, peer review, and CI/CD, with configuration managed as code
Prepare AI-ready data, including feature tables, training data sets, and RAG content (chunking, embeddings, vector indexes)
Publish governed data to operational systems through APIs, events, and reverse ETL, with versioned interface contracts
Support production operations (monitoring, root cause analysis, service levels) and maintain documentation and runbooks for handoff to managed services
Skills and Competencies
Strong problem-solving skills, with a focus on root cause over quick fixes
Clear communication with both technical and non-technical partners
A standards-first mindset that favors reusable frameworks and automation
Education and Experience
Required Qualifications
Bachelor’s degree in Computer Science, Mathematics, Physics, Engineering, or equivalent experience required
5 + years of data engineering experience, including production ELT pipelines on a cloud data platform required
Hands-on Databricks experience (Spark, Delta Lake, orchestration, Unity Catalog), preferably on Azure
Experience building framework-based ingestion within a medallion architecture
Advanced Python (including PySpark and automated testing) and SQL
Strong data modeling skills across dimensional, normalized, and lakehouse patterns
Practical data governance experience: profiling, data quality, MDM, catalog, and lineage
Experience with Git and CI/CD for data assets
Preferred Qualifications
2+ years of Consumer Packaged Goods analytics experience preferred
Broader Azure stack experience (ADLS, Data Factory, Fabric, Power BI); Databricks certification
Governance or catalog tools such as Purview, Collibra, Alation, or Informatica
Streaming (Kafka, Structured Streaming) and infrastructure as code
Experience preparing data for ML or generative AI applications
Experience on agile teams distributed across geographies
Compensation and Benefits
The U.S. base salary range for this full-time position is $92,500– $131,700. This position is also eligible for a bonus.
As a Church & Dwight US employee, you (and eligible dependents, as applicable) will have access to medical, dental, vision, basic life insurance, paid vacation and sick time, and Paid Parental Leave. U.S. employees are entitled to paid holidays, floating holidays, and vacation days starting in their first year of employment depending on hire date. You are also able to participate in our 401k retirement plan (with company match and profit-sharing) and Discounted Employee Stock Purchase Plan.
The actual base pay offered to the successful candidate will be based on multiple factors, including, but not limited to, job-related knowledge/skills, experience, business needs, geographical location, and internal pay parity. Compensation decisions are dependent upon the facts and circumstances of each position and candidate.
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Church & Dwight is proud to be an Equal Opportunity Employer/Veterans/Individuals with Disabilities.
For more information on our company, our brands and our culture visit us at http://www.churchdwight.com/
