Associate Director, Data & Analytics

Grant Thornton•Chicago, IL

2d•$172,000 - $250,000

About The Position

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.

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 security, access management, privacy, data retention, archival patterns, and governed data delivery practices.
Experience working with business stakeholders, application teams, architects, security, infrastructure, vendors, and delivery partners to translate requirements into implemented data solutions.
Ability to work in a fast-paced integration environment with multiple priorities, dependencies, delivery milestones, and business continuity requirements.
Strong problem-solving, communication, documentation, and stakeholder management skills, with the ability to explain technical concepts clearly to both technical and business audiences.
Bachelor’s degree in Information Systems, Computer Science, Engineering, Data Analytics, Business, or related field required; advanced degree or relevant cloud/data certifications preferred.

Nice To Haves

Experience supporting merger, acquisition, divestiture, or acquired-company technology integration programs.
Experience with cloud data platforms and tools such as Snowflake, Azure Data Factory, Databricks, dbt, Informatica, Fivetran, Matillion, Airflow, or comparable data engineering technologies.
Experience with data archival, historical data retention, legacy system decommissioning, or compliance-driven data preservation initiatives.
Familiarity with analytics and reporting platforms such as Power BI, Tableau, Qlik, or comparable business intelligence tools.
Experience operating in a professional services, consulting, accounting, or complex global enterprise environment.
Experience developing reusable pipeline frameworks, engineering standards, data platform runbooks, and operational support models.

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.