Data Engineer

Church & Dwight is building analytics foundations that can scale from ingestion to governed, AI-ready data products. As a Data Engineer in the Analytics Engineering Group, you will help design and operate ingestion frameworks, ELT pipelines, and curated datasets on a Databricks lakehouse using a Bronze/Silver/Gold (medallion) approach. The work includes quality controls, lineage and cataloging, and publishing governed data for downstream operational use.

What you’ll do

• Create reusable, metadata-driven ingestion frameworks and build batch and streaming ELT pipelines that move data through Bronze, Silver, and Gold layers using defined quality gates.
• Implement incremental and CDC processing with restartable, idempotent patterns to support reliable reruns.
• Design dimensional, normalized, and data vault models, and deliver curated Gold data products with conformed dimensions that reconcile across regions and source systems.
• Profile data sources and add automated data quality and reconciliation controls, while supporting master and reference data management.
• Maintain the semantic layer, catalog, lineage, and business glossary in partnership with data stewards; extend classification, masking, and access controls for AI use cases.
• Build and optimize solutions on Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, Workflows, and Unity Catalog, including performance and cost improvements and contributions 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 via APIs, events, and reverse ETL, using versioned interface contracts.
• Support production operations with monitoring, root cause analysis, and service levels, and maintain documentation and runbooks for handoff to managed services.

Required qualifications

• Bachelor’s degree in Computer Science, Mathematics, Physics, Engineering, or equivalent experience.
• 5+ years of data engineering experience, including production ELT pipelines on a cloud data platform.
• Hands-on Databricks experience (Spark, Delta Lake, orchestration, Unity Catalog), preferably on Azure.
• Experience building framework-based ingestion in 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.

Technologies you’ll work with

• Databricks, PySpark, Spark SQL, Delta Lake, Delta Live Tables, Workflows, Unity Catalog
• Python, Git, CI/CD, APIs
• Kafka, Structured Streaming
• Azure, ADLS, Data Factory, Fabric, Power BI
• Purview, Collibra, Alation, Informatica
• Infrastructure as code, Reverse ETL

Compensation

USD 92,500 - 131,700 per year.

Benefits

• Medical, dental, vision
• Basic life insurance
• Paid vacation and sick time
• Paid Parental Leave
• Paid holidays and floating holidays
• 401k retirement plan (with company match and profit-sharing)
• Discounted Employee Stock Purchase Plan

Work environment

Hybrid role based in Ewing, NJ. In-person attendance is required on Tuesdays, Wednesdays, and Thursdays, with remote work options on Mondays and Fridays.

Preferred qualifications

• 2+ years of Consumer Packaged Goods analytics experience.
• Broader Azure stack experience (ADLS, Data Factory, Fabric, Power BI) and Databricks certification.
• Governance or catalog tools experience such as Purview, Collibra, Alation, or Informatica.
• Streaming (Kafka, Structured Streaming) and infrastructure as code experience.
• Experience preparing data for ML or generative AI applications.
• Experience on agile teams distributed across geographies.