Sr Director, Data Engineering & Architecture

Our firm has been engaged by a growing, data-centric healthcare technology and services organization that supports customers through innovative healthcare cost management, member experience, and claims-related solutions. The company operates in a complex, highly regulated environment where technology, data, integration, and operational execution are critical to the business.

The organization is continuing to modernize its enterprise data environment and is looking to add a Senior Director of Data Engineering & Architecture to define technical direction, strengthen the data engineering organization, and accelerate delivery of enterprise-grade data solutions. The team has strong people who can identify problems, but this role requires a leader who has seen proven architecture patterns work at scale and can confidently guide the organization toward solutions that are practical, sustainable, and appropriate for the business.

The right person will combine strong data architecture judgment with practical leadership, helping the team choose proven approaches, avoid unnecessary reinvention, and move major initiatives from ambiguity to execution. This person will also help shape the team, partner with senior business leaders, and oversee outside technology partners when additional investment is used to accelerate delivery.

This is a full-time, direct-hire position that can be based remotely in the United States and must be able to work Eastern Time Zone hours.

The Role You’ll Play

The Senior Director of Data Engineering & Architecture will own the direction and evolution of the enterprise data ecosystem. This person will bring the depth of experience to recognize proven solution patterns, understand where teams commonly run into trouble, and guide engineers toward approaches that can scale without repeatedly going back to the drawing board.

The current environment is Microsoft-oriented, including Azure, Azure Data Factory, Microsoft Fabric, OneLake, SQL Server, and Synapse. The strongest candidates will understand modern data platform concepts such as medallion architecture, semantic modeling, data pipelines, analytics consumption patterns, and self-service access to data. Experience with other enterprise platforms such as Databricks or Snowflake is valuable because this leader should be able to evaluate technology choices objectively and evolve the architecture over time.

This role will work closely with technology leadership, analytics, business operations, and executive stakeholders to make the enterprise data environment more useful to the people consuming it. That includes traditional reporting and analysis, ad hoc analytics, and emerging opportunities to use AI and self-service tools so business users can interact with enterprise data more directly.

Key responsibilities include:

• Lead, assess, and shape the data engineering team, including evaluating skills, clarifying roles, identifying gaps, developing strong performers, and building a more cohesive organization.

• Define enterprise data architecture direction using proven, enterprise-grade patterns and provide the technical judgment needed to avoid repeated redesign or first-time experimentation where established approaches already exist.

• Design and guide solutions across medallion architecture, semantic modeling, data pipelines, integration, reporting enablement, ad hoc analytics, and other core data engineering capabilities.

• Guide the current Microsoft data platform while evaluating where technologies such as Databricks, Snowflake, or other platforms could add value over time without forcing unnecessary wholesale migrations.

• Partner with analytics teams, business users, and executive stakeholders to improve how data is consumed, including self-service analytics and AI-assisted access to enterprise information.

• Lead major third-party acceleration initiatives when outside investment is used to advance the data platform, including partner selection, delivery oversight, timeline and budget accountability, knowledge transfer, and long-term sustainability after the partner exits.

• Coach and develop engineers while establishing clear expectations around quality, delivery, ownership, engineering standards, and accountability.

• Communicate clearly with senior technology and business leaders about priorities, architecture decisions, risks, tradeoffs, dependencies, and recommendations.

Background Profile:

The successful candidate will be a seasoned data engineering and architecture leader who has worked around mature enterprise data environments and has seen multiple approaches succeed or fail in practice. This role requires someone who can recognize established solution patterns, make sound architectural decisions in context, and provide a level of technical judgment that comes from having been around complex enterprise implementations before.

The ideal leader will also have experience building and reshaping technical teams, working with analytics consumers, and managing strategic third-party engagements used to accelerate major initiatives. Healthcare experience is helpful, but not required. Experience in financial services or another sophisticated, regulated enterprise environment can translate well, particularly when it has exposed the candidate to mature data architecture and operating models.

The ideal background includes:

• 10+ years of progressive experience in data engineering, enterprise data architecture, or large-scale data platform development, including meaningful leadership responsibility.

• 5+ years directly leading data engineering teams, with experience assessing talent, shaping team structure, mentoring engineers, and addressing capability gaps.

• Deep understanding of modern enterprise data architecture, including medallion patterns, semantic modeling, pipelines, integration, data transformation, reporting enablement, and analytics consumption.

• Strong knowledge of Microsoft cloud and data technologies such as Azure, Azure Data Factory, Microsoft Fabric, OneLake, SQL Server, and Synapse, combined with exposure to other platforms such as Databricks or Snowflake.

• Demonstrated ability to define technical direction, apply proven enterprise patterns, communicate architectural tradeoffs, and translate complex business needs into practical, sustainable solutions.

• Experience leading significant third-party or consulting engagements where an outside partner accelerated delivery, transferred knowledge, and left the internal organization capable of sustaining the solution.

Preferred qualifications:

• Experience in healthcare technology, claims management, self-funded benefits, insurance, financial services, or another complex regulated environment.

• Experience leading analytics or data science teams, or designing data platforms around the needs of analysts, reporting teams, and self-service business users.