Data Engineer

Worldpac•Oak Brook, IL

22d•Onsite

About The Position

Worldpac, a leading name in automotive parts distribution, is looking for a results-driven Data Engineer to help build the robust and scalable data pipelines and infrastructure that underpin our digital transformation and AI initiatives. The Data Engineer will play a critical role in designing, implementing, and maintaining data infrastructure that supports data science, analytics, and operational systems across Worldpac’s core business functions. This role focuses on developing production-grade data pipelines that integrate with a wide range of systems—from modern APIs to legacy databases—ensuring data is reliable, clean, and ready for downstream use. Ideal candidates will bring a strong foundation in data engineering, experience working with cloud and legacy systems, and a passion for applying best practices in data architecture, software development, and automation. This position will be onsite at our Corporate HQ in Oak Brook, IL 5 days/week.

Requirements

4+ years of experience in data engineering or software development with a focus on data
Proficient in SQL and Python (or Scala) for developing data workflows and transformations
Hands-on experience with cloud data platforms (e.g., Snowflake, Databricks, BigQuery, Redshift)
Experience with orchestration tools (e.g., Airflow, Prefect, Dagster) and CI/CD pipelines
Solid understanding of data warehousing concepts, schema design, and performance optimization
Ability to work with structured and unstructured data across batch and streaming paradigms

Responsibilities

Data Pipeline Development and Management: Build and maintain robust ETL/ELT pipelines to ingest data from diverse systems (ERP, CRM, supply chain, sales, external APIs, etc.).
Create and manage synthetic and derived features to support AI and analytics use cases.
Ensure data quality, consistency, and lineage across all pipeline stages.
Systems Integration and Modernization: Connect legacy systems and databases with modern cloud-based platforms (e.g., Snowflake, Databricks, AWS).
Engineer real-time and batch data flows, including support for streaming data where appropriate (e.g., Kafka, Kinesis).
Partner with IT to improve access to core systems and modernize data interfaces.
Data Governance and Reliability: Implement monitoring, validation, and observability frameworks for data flows.
Ensure compliance with data privacy, retention, and security policies.
Maintain documentation for data sources, transformations, and pipeline dependencies.
Collaboration and Enablement: Work closely with data scientists and analysts to translate business and model requirements into engineered datasets.
Create reusable data assets and modular pipelines to support experimentation and production deployment.
Contribute to team standards for code quality, version control, and DevOps practices.