Senior Data Engineer, Growth, Insights & Analytics

Edgewell Personal Care Brands, LLC•Shelton, CT

1d•$112,000 - $168,000•Hybrid

About The Position

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). This position will have a hybrid (3x/week) at either our Shelton, CT or NYC office

Requirements

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 CitrusAd (Criteo Retail Media); Paid media platforms (Google, Meta, TikTok)
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)

Responsibilities

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 BigQuery, 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