Data Engineer

Church & Dwight Co., Inc.•Princeton, SC

3d•$92,500 - $131,700•Hybrid

About The Position

Church & Dwight is seeking a Data Engineer to join its Analytics Engineering Group. The role builds and maintains ingestion frameworks, ELT pipelines, data models, and curated data products that deliver insights to business functions worldwide. The position centers on a Databricks lakehouse using a Bronze/Silver/Gold medallion architecture, with an emphasis on reusable, metadata-driven engineering, strong governance, and AI-ready data. The Data Engineer partners with analytics engineers, architects, data scientists, data stewards, and business analysts, and works across time zones with a global managed services team. This is a hybrid role based in Ewing, New Jersey. In-person attendance is required Tuesdays, Wednesdays, and Thursdays; remote work is available Mondays and Fridays.

Requirements

Bachelor’s degree in Computer Science, Mathematics, Physics, Engineering, or equivalent experience.
At least 5 years of data engineering experience, including production ELT pipelines on a cloud data platform.
Hands-on Databricks experience with Spark, Delta Lake, orchestration, and Unity Catalog; Azure experience is preferred.
Experience building framework-based ingestion within a medallion architecture.
Advanced Python, including PySpark and automated testing, and advanced SQL.
Strong data modeling skills across dimensional, normalized, and lakehouse patterns.
Practical data governance experience, including profiling, data quality, MDM, catalog, and lineage.
Experience with Git and CI/CD for data assets.
Strong problem-solving skills, with a focus on root cause rather than quick fixes.
Clear communication with technical and non-technical partners.
A standards-first mindset that favors reusable frameworks and automation.

Nice To Haves

At least 2 years of Consumer Packaged Goods analytics experience.
Broader Azure stack experience, including ADLS, Data Factory, Fabric, or Power BI; Databricks certification.
Experience with governance or catalog tools such as Purview, Collibra, Alation, or Informatica.
Experience with streaming technologies such as Kafka or Structured Streaming, and infrastructure as code.
Experience preparing data for machine learning or generative AI applications.
Experience on agile teams distributed across geographies.

Responsibilities

Build reusable, metadata-driven ingestion frameworks and batch and streaming ELT pipelines that move data through Bronze, Silver, and Gold layers, with 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 data 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 using PySpark, Spark SQL, Delta Lake, Delta Live Tables, Workflows, and Unity Catalog; optimize performance and cost and contribute 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 datasets, and RAG content such as chunking, embeddings, and vector indexes.
Publish governed data to operational systems through APIs, events, and reverse ETL, using versioned interface contracts.
Support production operations, including monitoring, root cause analysis, and service levels; maintain documentation and runbooks for handoff to managed services.