Data Lineage Analyst - Fully Remote/ No C2C

Job Title: Data Lineage Analyst
Location: Remote (USA)
Duration: Contract (4+ months)

Keywords: Data Lineage, Data Analyst, Source-to-Target Mapping, SQL, ETL, Data Warehouse, Databricks, Data Governance, Data Profiling, Data Reconciliation.

< style="line-height: 15.0pt;">Job Summary

We are seeking a Data Lineage Analyst to analyze legacy data warehouse assets, identify authoritative source systems, trace end-to-end data lineage, document transformation logic, and develop source-to-target mapping specifications for migration to Databricks. The ideal candidate will work closely with business SMEs, data owners, and engineering teams to ensure data accuracy, traceability, and successful modernization initiatives.

< style="line-height: 15.0pt;">Key Responsibilities

• Analyze legacy warehouse tables, columns, metadata, and business definitions.
• Trace data lineage from downstream warehouse objects to authoritative source systems.
• Review ETL/ELT processes, SQL code, stored procedures, and data mappings to identify transformation logic.
• Perform data profiling, reconciliation, and validation of lineage findings.
• Create and maintain source-to-target mappings, lineage documentation, and data migration requirements.
• Collaborate with SMEs, data stewards, and engineering teams to validate findings and support Databricks implementations.

< style="line-height: 15.0pt;">Required Skills

• Experience with Data Lineage, Data Mapping, and Source-to-Target Analysis.
• Strong SQL and data analysis skills.
• Knowledge of ETL/ELT processes, data warehousing, and metadata analysis.
• Experience with data profiling, reconciliation, and validation techniques.
• Familiarity with Databricks and modern data platform migrations.
• Strong documentation, analytical, and stakeholder communication skills.

< style="line-height: 15.0pt;">Preferred Qualifications

• Experience in Healthcare, Insurance, or regulated data environments.
• Exposure to data governance, business glossaries, and data quality frameworks.
• Understanding of enterprise data architecture and integration patterns.