Senior Data Engineer – Philadelphia, 19109

Senior Data Engineer – Philadelphia, 19109, United States of America

How we LEAD:

We are seeking an experienced and driven Senior Data Engineer Enterprise Data Products within the Global Data & Analytics team.

You are passionate about building scalable, reliable, and AI-ready data platforms that power enterprise decision-making and advanced analytics. You understand that modern data platforms must not only support reporting, but also enable machine learning, generative AI, and intelligent applications through high-quality, well-governed, and semantically consistent data.

In this role, you will lead teams of data engineers responsible for building and operating domain-specific data products, semantic layers, and data marts that serve as trusted, AI-ready sources of truth across the organization, with a strong emphasis on ecommerce data products and Shopify data pipelines.

You will play a key role in shaping how data is structured, governed, and exposed to support AI/ML use cases, feature engineering, and semantic consistency across tools and applications.

How youll CREATE:

• Design and build scalable data pipelines and data products that support analytics and AI/ML use cases.

• Build and scale ecommerce data products and pipelines, with particular focus on Shopify data such as orders, customers, products, transactions, fulfillment, and marketing events.

• Develop and maintain AI-ready datasets, ensuring high quality, consistency, and usability for downstream applications.

• Work with data scientists and analysts to support feature engineering, model training data, and experimentation needs.

• Contribute to building and maintaining semantic layers that standardize business definitions and enable consistent reporting and analysis.

• Implement data validation, testing, and monitoring to ensure data quality and reliability.

• Build and optimize data models (dimensional, curated layers) aligned with business requirements.

• Optimize data storage and query performance by leveraging columnar data structures and databases where applicable.

• Support batch and real-time data pipelines using modern data stack tools.

• Follow best practices for code quality, version control, and CI/CD in data engineering workflows.

• Collaborate with product, analytics, and governance teams to ensure data is well-documented, discoverable, and trusted.

• Troubleshoot and resolve data issues, ensuring minimal disruption to downstream users.

Bring your VIBE:

• 58 years of experience in data engineering or related roles.

• Strong experience building data pipelines, data models, and data platforms.

• Experience working with Shopify data is strongly preferred, including Shopify orders/customers/products/fulfillment schemas

• Proven experience handling ecommerce data at scale, including orders, carts, customers, product/catalog, inventory, fulfillment, payments, refunds/returns, promotions/discounts, subscriptions, web/app clickstream, and marketing attribution.

• Preferred: experience handling Shopify data and ecommerce datasets, including ingesting, modeling, validating, and reconciling commerce data across platforms.

• Familiarity with AI/ML data requirements, including preparing datasets for model training and analytics.

• Hands-on experience working with or contributing to semantic layers (e.g., dbt, LookML, etc.).

• Solid understanding of data warehousing, dimensional modeling, and modern data architectures.

• Experience with cloud platforms (AWS, Azure, or GCP) and tools like Spark, Snowflake, Databricks, or similar.

• Strong SQL skills and proficiency in Python or similar language.

• Experience working with columnar databases or columnar storage formats (e.g., BigQuery, Redshift, Snowflake, Parquet) and optimizing query performance.

• Experience implementing data quality checks and monitoring.

• Ability to work cross-functionally and translate requirements into technical solutions.

• Preferred candidate location: Philadelphia, PA (Philly).

Tagged as: Attribution Modeling, BigQuery, Looker, Python, Snowflake, SQL