Sr Data Engineer Insights & Analytics

Reporting to the Sr Director, Media Analytics, the Sr Data Engineer is responsible for building and scaling the data infrastructure that enables insights, analytics, and decision-making across Edgewell’s portfolio of brands. This role will lead the development of modern, cloud-based data platforms and pipelines that unify complex data sources (e.g., retail POS, syndicated data, retail media, e-commerce, and supply chain) into a trusted, accessible foundation for the organization.

This individual should have a passion for building scalable data solutions that drive business impact. The ideal candidate is a self-starter and able to communicate with and work across multiple teams. This role requires both strong technical expertise and the ability to translate business needs into actionable data solutions that support brand growth, digital commerce, and retail media effectiveness. In this role as lead developer, the majority of data streams and ETL work will be accomplished leveraging SQL and Python within current stack (Azure, Snowflake, dbt, PowerBI).

Hybrid work: 3 days per week at either our Shelton, CT or NYC office.

• Lead and own the design, development, and optimization of scalable cloud-based data pipelines and architectures (AWS, Azure, or GCP) to support enterprise analytics, reporting, and data science initiatives
• Build and manage a modern data platform (lakehouse architecture) leveraging technologies such as Snowflake, Databricks, Redshift, or Big Query, enabling high-performance analytics and data accessibility
• Collaborate with IT and Architecture teams to align on cloud strategy, data infrastructure, and enterprise technology roadmaps

• Integrate and manage diverse data sources, including retailer POS, syndicated data (e.g., Nielsen/Circana), e-commerce, ERP systems, and retail media platforms (e.g., Amazon Marketing Cloud, Walmart Luminate, Kroger Precision Marketing)
• Develop and optimize pipelines for ingesting and transforming retail media, digital advertising, and campaign performance data (e.g., Amazon Ads, DSP, paid search, social platforms)
• Develop scalable and reusable data models, APIs, and curated datasets to support self-service analytics and business intelligence tools (e.g., Power BI)

• Ensure high standards of data quality, governance, security, and privacy compliance, implementing monitoring, metadata management, and lineage tracking
• Continuously evolve data engineering capabilities, including adoption of real-time/streaming pipelines (Kafka, Spark Streaming), automation frameworks, and AI-ready data infrastructure

• Bachelor’s degree required; advanced degree preferred
• 6–8+ years of experience in Data Engineering, Data Platforms, or a related technical field
• Experience designing and building cloud-native data pipelines and architectures (ETL/ELT)
• Strong proficiency in SQL and Python (or similar languages such as Scala or Java)
• Hands‑on experience with cloud platforms (AWS, Azure, or GCP) and modern data stack tools including Snowflake, Databricks, Airflow, dbt, Fivetran, or similar
• Experience working with API‑based data ingestion and integration frameworks, particularly for digital media and retail platforms
• Demonstrated ability to transform complex, high‑volume data into clean, scalable, and usable datasets
• Experience implementing data governance, quality frameworks, and observability tools
• Strong communication and stakeholder management skills, with the ability to translate technical concepts into business‑friendly language
• Familiarity with CPG and omnichannel data ecosystems, including retailer POS, syndicated data (IRI/Nielsen/Circana), and digital commerce data sources
• Hands‑on experience with retail media and advertising data platforms, including:
  Amazon Marketing Cloud (AMC);
  Amazon Ads / DSP;
  Walmart Luminate, Kroger Precision Marketing, or Citrus Ad (Criteo Retail Media);
  Paid media platforms (Google, Meta, Tik Tok)
• Experience enabling MMM, retail media measurement, or marketing analytics use cases
• Knowledge of data modeling techniques for marketing, media, and commercial analytics
• Experience with real‑time or streaming data pipelines (Kafka, Spark, or similar)