Principal Data Architect/Engineer (Cloud Agnostic & Governance Lead) || Chicago, IL (1 Day per week onsite)

Job Description:  

JO Details

Job Title: Principal Data Architect/Engineer (Cloud Agnostic & governance Lead)

Onsite Requirement/Remote : 1 day/week in Chicago

Background (Project/Initiative + Why the role is open): Immediate need to lead a modernization effort for new data platform (lift and shift) 

Submittal Requirements

3-5 Must Haves (need to be highlighted in sizzle & present on resume) 

• 20+ years of experience required (non-negotiable),  within a Fortune 500 environment
• Strong proficiency in AWS (Amazon Web Services)
• Familiarity with Google Cloud Platform (nice to have, not required)
• Experience supporting large-scale modernization initiatives, including lift-and-shift migrations
• Ability to establish policies and procedures, with a balance of leadership and hands-on execution

TECHNICAL ALIGNMENT

• AWS (Glue, EMR, Redshift, S3)
• Data lakehouse architecture experience
• Data governance and DataOps
• ETL/ELT and large-scale data pipelines
• Exposure to multi-cloud (nice to have) 

Job Description

We are seeking a Principal Data Architect  to serve as the highest-level technical authority and strategic influencer within our Enterprise Data Infrastructure team. This is a high-visibility, high-impact role designed to sit above our current senior engineering tier. You will act as the critical bridge between long-term business strategy and tactical technical execution, directly influencing how our enterprise handles petabyte-scale data processing for 2026 and beyond.

In this role, you will lead the architectural evolution of our data ecosystem. Our immediate interim roadmap involves migrating from a legacy, scheduled Redshift environment to a modern, decoupled AWS Lakehouse architecture utilizing AWS Glue, EMR Serverless, and Amazon Redshift, alongside high-frequency Oracle Fusion Cloud integrations via Fivetran. However, a primary focus of this role will be future-proofing our architecture. You will design vendor-agnostic frameworks (utilizing open table formats like Apache Iceberg) to ensure seamless portability to meet evolving business requirements and minimize vendor lock-in.

Beyond technical vision, a massive component of this role is governance and mentorship. You will establish automated guardrails and DataOps pipelines to scale engineering quality across a combination of on-shore architects and engineers, and off-shore developers and operations support personnel.  Concurrently, you will mentor our onshore team of 3 intermediate and 3 senior data engineers/architects, elevating their technical capabilities and refining their leadership and soft skills.

Key Responsibilities

1. Strategic Architecture & Future-Proofing

• Cloud-Agnostic Vision: Design and execute a 3-to-5-year enterprise data warehouse modernization roadmap that prioritizes extreme data portability, decoupling storage from compute using open table formats (e.g., Apache Iceberg).
• Multi-Cloud Readiness: Evaluate alternative ecosystems and ensure current AWS implementations are built with vendor-neutral patterns, avoiding proprietary lock-in.
• AI & Semantic Preparation: Establish strict semantic conventions, data hygiene standards, and metadata graphs today to ensure the underlying data warehouse is optimized for future conversational AI agents and modern BI tools.

2. Engineering Governance & Quality Automation

• Scale Through Guardrails: Stop technical debt at the source by designing reusable framework templates and abstracted wrappers that enforce coding best practices across an offshore development team
• Automated DataOps Gates: Implement automated CI/CD quality gates (e.g., automated SQL linting, schema drift detection, and data validation frameworks) to catch low-quality code before it reaches human review.
• Operational Excellence: Redefine the onshore team''s workflows, shifting senior engineers from manual code-reviewers to platform product owners.
• Code modernization: Assist in setting policies and training for the team to move from working exclusively in SQL to also including Spark jobs. Help us figure out the proper handoff mechanisms and frameworks for evaluating SQL developed solutions to Spark jobs.

3. Ingestion & Pipeline Optimization

• High-Frequency Ingestion: Own the architectural pattern for high-frequency source replication, ensuring optimal performance without violating API limits or driving unnecessary cloud compute costs.
• Modern Orchestration & Storage: Guide the transition from rigid, scheduled jobs to event-driven processing using AWS Glue, EMR Serverless, and optimized presentation layers in Redshift.

4. Mentorship & Leadership

• Talent Elevation: Act as a dedicated mentor to the 3 intermediate and 3 senior onshore engineers, pushing them to think in terms of holistic system design and enterprise scalability.
• Soft Skills Coaching: Help senior staff develop critical soft skills, including "influence without authority," cross-functional stakeholder communication, and effective offshore vendor governance.

Required Qualifications

• 8+ years of deep experience in enterprise data engineering, data architecture, or data platform roles, with at least 2+ years operating at a Staff, Principal, or Lead Architect level within a Fortune 500 scale environment.
• Expertise in Cloud-Agnostic Design: Proven track record of building data lakehouses centered around open table formats (e.g., Apache Iceberg, Delta Lake) to ensure cross-cloud compatibility.
• Advanced AWS Data Ecosystem Experience: Hands-on architectural experience with AWS Glue, EMR Serverless, S3 data lakes, and Amazon Redshift.
• Governance at Scale: Demonstrated success implementing automated testing, CI/CD pipelines, and DataOps frameworks (e.g., dbt, Great Expectations, SQLFluff) to govern large delivery teams.
• Complex Ingestion Mastery: Experience managing high-frequency CDC data ingestion from massive enterprise ERP systems (specifically Oracle Fusion Cloud or equivalent) using modern tools like Fivetran.
• Expert Coding Skills: Mastery of Python and complex SQL, with a deep understanding of query optimization, data modeling (Kimball, Data Vault 2.0), and technical debt remediation.