Principal Data Architect / Engineer

Job: Principal Data Architect / Engineer

Location: Chicago, IL, 60661

Duration: 6 Months contract

Seeking a Principal Data Architect / Engineer to serve as the top technical authority within an Enterprise Data Infrastructure team. This is a high-visibility role bridging long-term business strategy and hands-on execution, helping shape how large-scale data is managed for the future. Will lead the evolution from a legacy Redshift environment to a modern AWS-based lakehouse architecture (Glue, EMR Serverless, Redshift) while integrating high-frequency Oracle Fusion data via Fivetran.

A key focus will be building a future-proof, vendor-agnostic architecture using open formats (e.g., Apache Iceberg) to ensure flexibility and minimize lock-in. Beyond architecture, this role is heavily focused on governance and leadership. Will establish DataOps standards, automated quality controls, and scalable engineering practices across both onshore/offshore team. Will mentor a team of intermediate and senior engineers, elevating both technical capability and leadership effectiveness.

Key Responsibilities:

Define and execute a 3-5 year data modernization roadmap. Design cloud-agnostic architecture with strong data portability.

Implement DataOps pipelines, CI/CD quality gates, and coding standards.

Improve engineering discipline and shift teams toward platform ownership

Lead transition from scheduled processes to more modern, event-driven systems

Optimize data ingestion and pipeline performance Mentor and develop engineering team, including communication and leadership skills

Required Qualifications

20 years in data engineering or architecture, with leadership experience at a senior level

Strong AWS experience (Glue, EMR, S3, Redshift) Success building scalable, governed data platforms

Expertise in Python, SQL, and data modeling Experience with large-scale data ingestion (ERP systems, Fivetran or similar)

Preferred: Multi-cloud experience (Google Cloud Platform, Snowflake) Exposure to AI-ready data models and semantic layers Familiarity with infrastructure as code (Terraform, Docker,