Job Description — Enterprise Data Architect / Data

Governance Lead

Overview

We are seeking an Enterprise Data Architect / Data Governance Lead to establish, strengthen,

and mature our enterprise data architecture, data governance, and data quality capabilities. The

organization is moving significant workloads to Azure while maintaining key assets in AWS and

Databricks.

This role will bring clarity, standards, and hands-on partnership to modernize the data ecosystem. It is

a results-driven position working directly with key stakeholders and our Enterprise Architecture

team. You will be embedded with engineering leaders to deliver practical, immediately valuable

solutions and build a cohesive strategy across a hybrid Azure/AWS environment, including guidance

on our data warehouse migration/modernization.

Key Responsibilities

Define and evolve enterprise data architecture principles, reference models, and reusable

standards.

Design the target-state data architecture for a hybrid Azure/AWS ecosystem leveraging

Databricks, ensuring scalability, resilience, and cost efficiency.

Lead data modeling (conceptual/logical/physical), data services design, and modern scalable

pipeline patterns.

Establish and operate a Data Governance framework, including ownership models,

stewardship, controls, decision rights, metadata standards, lineage, and catalog adoption.

Define and implement Data Quality standards, metrics, thresholds, and continuous

monitoring approaches.

Partner directly with Engineering Leads to co-design solutions, translating strategy into

actionable roadmaps and backlogs.

Support and guide data warehouse migration/modernization programs, including

incremental migration strategies, dependency management, and risk mitigation.

Identify and deliver quick wins that improve architecture, governance, and quality in

environments with legacy systems and limited structure.

Produce clear architecture documentation, decision records, best practices, and practical

enablement materials to drive adoption at scale.

Demonstrate pragmatic, non-academic thought leadership by showcasing art of the possible

through real, working solutions.

Required Skills & Experience

Strong experience defining enterprise data architecture principles and reference models.

Experience with Databricks or similar lakehouse platforms.

Hands-on background in modern cloud data ecosystems, including Azure and AWS.

Expertise in data modeling, data services design, and scalable data pipeline patterns.

Proven ability to establish Data Governance frameworks including ownership, stewardship,

controls, and decision rights.

Demonstrated capability defining and implementing data quality standards and monitoring.

Excellent communication skills and ability to work across a wide range of technical and

business stakeholders.

Ability to partner with engineering leaders to turn architecture strategy into prioritized,

actionable backlogs.

Experience supporting data warehouse migrations or modernization efforts.

Track record of delivering results in environments with legacy systems and multi-cloud

complexity.

Preferred Skills

Familiarity with Redshift, Azure Synapse, or other enterprise data warehouse technologies.

Background in hybrid cloud architectures and migration paths from AWS to Azure.

Strong understanding of financial services or regulated data environments.

Experience working with architecture teams supporting large-scale engineering

organizations.

Ability to influence through practical leadership and real-world delivery, not just theoretical

design.

Education

Bachelors degree in Computer Science, Information Systems, Engineering, or a related field.

Advanced degree or relevant certifications in cloud architecture, data engineering, or data

management are a plus.

Typical Profile

8+ years in data engineering, data architecture, or enterprise architecture roles.

Prior roles combining strategic architecture definition with hands-on delivery alongside

engineering teams.

Experience modernizing data ecosystems in enterprises with legacy platforms and multi-

cloud environments.

Core Competencies

Enterprise-level systems thinking and end-to-end data ecosystem view.

Pragmatic execution mindset: balancing target state with operational reality.

Strong stakeholder influence and alignment skills.

Ability to deliver incremental value while driving long-term strategy.

Standards-driven but flexible enough to evolve frameworks as the organization matures.