Data Engineering Director

RGP is hiring a Director, Data Platform & Integration to help grow and deliver work across their Digital Build & Integration practice. The role involves driving sales, serving as a solution architect for data platform and integration opportunities, and providing technical leadership throughout the delivery process.

Responsibilities:

• Drive new and expanded client work for the practice, with a focus on data platform, Master Data Management, and enterprise integration engagements
• Build relationships with senior client stakeholders including CDOs, enterprise architects, VPs of Data, and data platform owners across financial services, healthcare, and enterprise accounts
• Lead technical sales conversations from discovery through solutioning, proposal, close, delivery, and expansion, operating as the primary technical voice in pursuit situations
• Serve as the solution architect for data platform and integration engagements, owning the technical approach from first conversation through delivery
• Define architecture direction, platform fit, integration strategy, operational data model design, assumptions, risks, and pricing inputs across environments including Databricks, Snowflake, Azure Data Factory, Microsoft Fabric, and Azure Data Lake
• Bring a system-level perspective across the full data lifecycle, including ingestion, orchestration, integration, MDM, service-layer architecture, modeling, and consumption, to ensure solutions are coherent end to end and not just technically competent in isolation
• Translate complex, cross-platform data challenges into clear solution paths that CDOs, enterprise architects, and business stakeholders can understand, evaluate, and commit to
• Create and present proposals, estimates, solution narratives, executive recommendations, technical approaches, and delivery recommendations
• Provide technical leadership during delivery, including architecture decisions, solution direction, technical risk management, and quality of outcomes
• Guide delivery team members on technical approach and platform standards, stepping in to resolve architecture-level issues that affect scope, feasibility, or client confidence
• Use AI-native development approaches to rapidly explore, prototype, and validate solutions, converting ambiguous client needs into executable, high-value work faster than traditional discovery and design cycles allow
• Identify expansion opportunities through strong delivery, client trust, and a clear understanding of the client's broader data ecosystem needs
• Build reusable sales assets, solution frameworks, demos, estimation models, and delivery approaches that strengthen the practice's ability to pursue and close data platform work

Requirements:

• 10+ years of experience designing and delivering enterprise data platform solutions, including data pipelines, MDM implementations, service-layer integrations, and cloud data migrations
• 10+ years of experience in a professional services, consulting, systems integration, or client-facing delivery environment
• Deep, hands-on technical expertise across the full data lifecycle, with specific platform depth in two or more of the following: Databricks, Azure Data Factory, Snowflake, Microsoft Fabric, and Azure Data Lake
• Strong command of enterprise MDM patterns, service-layer integration architecture, and operational data model design in complex, multi-system environments
• Demonstrated ability to operate with a system-level perspective across ingestion, orchestration, modeling, and consumption layers, with the judgment to identify where architectural decisions in one layer create risk or constraint in another
• Proven ability to drive technical sales conversations independently with CDOs, enterprise architects, and data platform owners, including diagnosing the real problem, framing a solution, and advancing the opportunity through to close
• Comfortable moving between technical depth and business language in the same conversation, adjusting altitude without losing credibility at either level
• Track record of delivering large-scale data platform programs end-to-end with the delivery experience to know what makes an architecture realistic versus aspirational
• Hands-on experience using AI-native tooling to accelerate data platform delivery work, including pipeline scaffolding, schema and model generation, integration documentation, or automated data quality validation
• Prior delivery experience in financial services, healthcare, or other regulated industries where data governance, auditability, and compliance constraints shape platform and integration decisions is desirable
• Familiarity with federated data governance models, data mesh architecture, or enterprise data product strategies as applied to large, multi-domain environments is also desirable