Data Lineage & Governance Analyst

Role Description

• The Technical Analyst – Data Lineage will support a Data Governance, Controls, and Reporting program for a top-tier banking client.
• Responsible for establishing and validating end-to-end lineage across critical datasets used in operational and regulatory reporting.
• Translate governance and reporting requirements into actionable lineage deliverables (source-to-target mapping, lineage diagrams, metadata standards, and audit evidence).
• Work with Data Platform, Data Engineering, Architecture, Risk, Compliance, and Security teams to define lineage standards, metadata capture, and control points.
• Maintain lineage artifacts for Critical Data Elements (CDEs), key reports, and priority data products.
• Support control design to ensure traceability from source systems → transformations → curated layers → consumption (dashboards/reports/APIs).
• Actively participate from discovery workshops through to implementation and continuous improvement.
• Ensure traceability from data definition → transformation logic → lineage evidence → audit readiness.

• The role supports one of our top-tier banking clients in London (Canary Wharf) and requires a minimum of three days on-site presence.
• This is a permanent position based in the UK. We will only consider applicants who are eligible to work in the UK. For this role do NOT offer visa sponsorship.

• Minimum 3 years of relevant experience in data analytics, data quality, reporting controls, or data transformation programs (preferably in financial services).

• Minimum 3 years of relevant experience in data governance, lineage, metadata management, or controls programs within finance/banking.
• Strong understanding of data lineage concepts: technical lineage, business lineage, column-level lineage, impact analysis, and provenance.
• Hands‑on experience with data lineage / metadata tooling in enterprise environments (e.g., Collibra, Alation, Informatica EDC/IDMC, IBM Infosphere, Microsoft Purview, Apache Atlas, Amundsen, Data Hub or similar).
• Proven ability to build lineage for complex platforms: data lakes, warehouses, marts, and distributed processing (Spark‑based pipelines).
• Strong proficiency in SQL for tracing transformations and validating mappings across layers.
• Working knowledge of ETL/ELT patterns, data modeling (dimensional + normalized), and batch scheduling dependencies.
• Ability to interpret data transformation logic from pipelines (Spark SQL / PySpark / Hive queries / orchestration configs).
• Strong documentation capability: source‑to‑target mappings, lineage diagrams, data dictionaries, metadata standards, and control evidence packs.

• Strong proficiency in Python (data analysis/automation for metadata extraction, validation scripts, rule checks).
• Hands‑on experience with PySpark and Spark SQL in production environments.
• Solid knowledge of Hive, Impala, HDFS, and Parquet.
• Advanced SQL skills; experience with Oracle databases is preferred.
• Working knowledge of Autosys & Apache Airflow.
• Experience with CI/CD tools (Git, Harness, Urban Code Deploy (UCD), Red Hat Open Shift).
• Familiarity with AWS S3 for large-scale data storage.
• Exposure to Tableau (understanding data sources, extracts, dependencies) is a plus.

• Experience with regulatory reporting data domains (risk, liquidity, capital, finance, BCBS 239 alignment, etc.).
• Knowledge of data governance operating models: CDEs, data ownership, stewardship, data quality dimensions.
• Experience creating audit‑ready documentation and participating in audit walkthroughs.
• Experience working in Agile/Scrum delivery models.
• Familiarity with monitoring and alerting tools for data pipelines.

• Conduct discovery workshops to identify priority reports, data products, and Critical Data Elements (CDEs).
• Build and maintain end-to-end lineage across systems, including column‑level mappings where required.
• Produce and maintain Source-to-Target Mapping (STTM) documentation and metadata standards.
• Validate lineage accuracy by tracing logic through SQL/Spark transformations and pipeline configurations.
• Support impact analysis for proposed changes (upstream/downstream dependencies, report impact, control impact).
• Partner with engineers and platform teams to improve metadata capture and lineage automation (where possible).
• Define lineage‑related control points and produce audit‑ready evidence (diagrams, mappings, query proofs, run evidence).
• Support UAT by validating that reported numbers can be traced and explained back to trusted sources.
• Maintain the lineage backlog and track changes across releases to ensure artifacts remain current.