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 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.
Preferred Experience
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.
Success Measures
Data pipelines for acquired-company system integration are delivered in alignment with enterprise standards, scope, timeline, and quality expectations.
Snowflake archival solutions are reliable, governed, validated, discoverable, and structured to support retention, reporting, analytics, and future reuse.
Source-to-target mappings, data quality checks, reconciliation outcomes, pipeline documentation, and operational handoffs are complete and supportable.
Integration and archival activities protect business continuity during cutover, go-live, and post go-live hypercare.
Data engineering patterns are reusable, secure, cost-aware, and aligned with enterprise data architecture and governance expectations.
The base salary range for this position is between $172,000 and $250,000. Placement within the pay range is at Grant Thornton’s discretion, and it is based on multiple factors, including but not limited to, job-related knowledge/skills, experience, business needs, progression within the role, geographic location, and internal equity. At Grant Thornton, compensation decisions are dependent upon the facts and circumstances of each position and candidate.
