Data Engineer

Opportunity Information:

Overview:

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.

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.

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field
• Must-Have Skills:
• 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

Location Type: On-Site Pay Range: USD $103,500.00 - USD $138,000.00 /Yr.