Associate Director, Data

Grant Thornton is seeking a Data & Analytics Associate Director to join the team. Approved office locations can be found below. Associate Director, Data & Analytics – Position Summary: Grant Thornton GTech’s vision is centered around enabling scalable, secure, and repeatable data integration capabilities that support enterprise transformation, mergers and acquisitions, and modern analytics. The Data & Analytics function is responsible for designing and delivering governed data solutions that connect critical business and technology systems while enabling trusted reporting, archival, and analytics at scale. The Associate Director, Data & Analytics will be responsible for designing and implementing data pipelines within enterprise standards that support the integration of systems from acquired companies, as well as data archival in Snowflake. This role will partner with data architecture, enterprise architecture, application teams, security, infrastructure, implementation partners, and business stakeholders to deliver reliable, governed, and reusable data integration capabilities that support M&A integration, operational continuity, compliance, analytics, and long-term data asset management. Essential Duties &

Responsibilities:
Design and implement enterprise data pipelines that support the integration of systems, applications, and data assets from acquired companies. Build data ingestion, transformation, validation, and archival patterns that align with enterprise data architecture, security, governance, and operational standards. Implement data archival solutions in Snowflake that support acquired-company integration, historical reporting, data retention, compliance, and future analytics needs. Partner with data architecture and enterprise architecture teams to ensure pipeline designs follow approved architecture patterns, naming conventions, metadata practices, access controls, and integration standards. Collaborate with M&A integration, application, infrastructure, security, and business teams to understand source-system requirements, data dependencies, integration timelines, archival needs, and target-state usage patterns. Translate business, technical, and integration requirements into data pipeline designs, data mappings, transformation logic, quality rules, and deployment plans. Develop and maintain scalable data engineering processes for batch, incremental, and event-driven data movement, where appropriate, across enterprise platforms and acquired-company systems. Establish data quality checks, reconciliation routines, exception handling, monitoring, and operational controls to ensure pipelines are reliable, auditable, and production-ready. Support data migration and archival activities by coordinating source extracts, transformation rules, target loading, validation outcomes, and issue resolution across internal and partner teams. Work with analytics and reporting stakeholders to ensure integrated and archived data assets are structured to support downstream business intelligence, analytics, AI, and operational reporting use cases. Partner with implementation vendors and technology teams to coordinate delivery activities, resolve blockers, validate deliverables, and ensure adherence to enterprise standards. Create and maintain technical documentation, including data flow diagrams, pipeline specifications, source-to-target mappings, operational runbooks, metadata, and support handoff materials. Support go-live readiness, cutover planning, data validation, post go-live hypercare, and transition of pipelines to steady-state data operations. Identify opportunities to improve data engineering patterns, automation, reuse, performance, cost efficiency, data observability, and operational supportability. Act as a data engineering leader within the Data & Analytics function, promoting strong engineering discipline, secure data handling, and consistent implementation practices. Other duties as assigned. Qualifications & Requirements 8+ years of experience in data engineering, data integration, data architecture, analytics engineering, application integration, or related enterprise technology roles. Demonstrated experience designing and implementing production data pipelines using modern ETL/ELT, orchestration, data warehousing, data lake, or cloud data platform technologies. Hands-on experience with Snowflake, including data loading, transformation, schema design, performance considerations, access patterns, and operational support concepts. Experience integrating data from multiple enterprise systems, acquired-company systems, SaaS platforms, ERP systems, operational applications, or legacy technology environments. Strong understanding of source-to-target mapping, data transformation, data validation, reconciliation, data quality, metadata, lineage, and pipeline monitoring practices. Knowledge of enterprise data architecture standards, data