Data Transformation Lead

Role Summary

The Data Transformation Lead is accountable for driving end‑to‑end data transformation initiatives—including cleansing, enrichment, modeling, validation, and migration—across cloud, MDM, analytics, and modernization programs. This role sits at the intersection of data engineering, governance, and business transformation, ensuring transformed data meets functional, quality, and compliance expectations. The Lead coordinates analysts, modelers, ETL developers, and platform SMEs to deliver clean, curated, and consumable data assets.

• Own and implement the data transformation approach
  , including cleansing, modeling, enrichment, and alignment to business logic.
  Define transformation pipelines and oversee execution across sprints—from raw to curated layers.
• Ensure transformation logic aligns with business rules, source-to-target mappings, and analytical needs.
• Oversee transformation layer design, ensuring standardized and governed patterns for data restructuring.

• Lead Data Analysts, Data Leads, Data Modelers, ETL Developers, DBAs
  , and other specialists in transformation-focused work streams.
  Drive alignment between technical teams and business stakeholders to ensure the transformation meets business requirements.
• Review and sign off on transformation design, data models, ETL mappings, and quality checks.

• Oversee data cleansing activities, standardization, and business-rule validation.
• Run or guide data quality validation routines comparing legacy vs target systems to detect anomalies and improve accuracy.
  Establish root-cause analyses for transformation errors and lead remediation efforts.
• Ensure DQ rules, thresholds, and exception handling are embedded throughout transformation processes.

• Guide the modeling of transformed datasets—including dimensional models, domain models, curated layers, and golden-record definitions.
  Validate mapping logic, transformation dependencies, lineage, and required enrichment attributes.
• Collaborate with data architects to ensure structure and standards align with cloud, MDM, or data warehouse strategies.

• Support cloud/MDM migration cycles by ensuring all transformed data meets ingestion, validation, and cutover criteria.
  Validate transformed datasets against migration manuals, ETL specs, and modeling documents.
• Work with integration teams to ensure transformed data is ready for downstream publishing, APIs, or analytical consumption.

• Ensure alignment to organization-wide data governance, data policies, and compliance guidelines.
  Partner with governance teams to ensure transformed data meets stewardship and metadata requirements.
• Drive adoption of new transformation frameworks, standards, and best practices—consistent with transformation leadership expectations.

• Communicate transformation progress, challenges, risks, and mitigation plans to program leadership.
• Run cross-functional workshops for business alignment, requirement clarification, and impact assessment.
• Enable collaboration between business process owners, technical SMEs, platform teams, and QA leads.

• Strong experience in data transformation
  , including cleansing, enrichment, data modeling, and validation.
  Hands-on or oversight-level expertise in ETL/ELT workflows, data pipelines, and cloud migration processes.
• Experience with profiling, DQ tools, or custom validation routines.
  Understanding of dimensional modeling, data warehouse concepts, and metadata standards.

• Proven ability to lead multi-disciplinary data teams.
• Experience managing complex data transformation efforts within modernization or migration programs.
• Strong ability to translate business rules into transformation logic and guide technical execution.
• Skilled in change management and driving adoption of new processes.

• Experience in cloud-based data platforms (Azure, AWS, GCP) or…