Banking Data Quality Analyst

Role Description

• The Techno-Functional Business Analyst will support a banking Data Quality / Data Under Governance program aligned to the proven UK DQP approach (PRA) and now being replicated across ECB and India regulatory asks.
• Support delivery across the five workstreams:
• CDE Identification (CDE inventory, definitions, ownership, scope by legal entity)
• SOR Allocation (authoritative source mapping, SoR/AR alignment, data contracts)
• Controls Mapping (control design, thresholds, risk appetite alignment, evidence requirements)
• Data Lineage (traceability across systems, transformation chains, endpoints/reporting)
• Operating Model (DCRM workflow, exception management, governance routines, reporting)
• Work with Markets, Risk, Finance, Operations, Technology, Data Platform, and Governance teams to drive outcomes and ensure regulatory alignment.
• Translate regulatory expectations into clear requirements and executable delivery artefacts (user stories, decision tables, STTMs, test packs).
• Ensure traceability from policy/regulatory expectation to CDE to SOR/AR to controls to lineage to exception management to audit evidence.
• Support validation readiness by producing clear, audit-ready documentation and evidence packs.

Location

• 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.

Experience Requirements & Qualifications

Core Experience

• Minimum 5 years of relevant experience in data governance, data quality, reporting controls, or data transformation programs (preferably in financial services / Capital Markets).
• Proven experience delivering governance-led programs involving CDEs, SOR/authoritative sources, controls, and lineage.
• Experience working in regulated remediation / regulatory delivery environments with exposure to validation, audit evidence, and structured governance.
• Strong stakeholder management across business, operations, technology, and governance functions.

Domain Knowledge

• Strong understanding of Capital Markets and Finance data domains (front-to-back awareness is a plus).
• Familiarity with risk appetite concepts as applied to data quality thresholds and control exceptions.

Technical / Analytical Skills

• Proficiency in SQL (advanced querying, reconciliation logic, data validation).
• Strong proficiency in Python for data analysis and automation (pandas, validation frameworks, scripting).
• Experience supporting or validating ETL/ELT pipelines and data quality frameworks (rules, thresholds, exception handling).
• Exposure to lineage and metadata approaches; ability to validate transformations and trace data across platforms.

Tooling / Delivery Methods

• Working knowledge of scheduling/orchestration tools such as Autosys and/or Apache Airflow (monitoring schedules, reruns, failure triage).
• Experience with CI/CD and release controls (Git, Harness, UrbanCode Deploy (UCD), Red Hat OpenShift or equivalent).
• Familiarity with large-scale storage patterns (e.g., AWS S3) for dataset movement and controls.
• Experience supporting BI/reporting outputs such as Tableau dashboards (data validation, extract refresh checks, reconciliation to source).

Nice-to-Have

• Experience with tools such as PySpark, Spark SQL, Hive, Impala, HDFS, Parquet, and Oracle databases.
• Hands-on exposure to DCRM tooling and operational exception management processes.
• Experience with governance/catalog tools and lineage documentation methods (Collibra/Alation/Informatica EDC/Purview or similar).
• Experience running delivery routines across workstreams (intake, triage, prioritization, wave planning, reporting).
• Experience working in Agile/Scrum delivery models.
• Familiarity with Visio (or equivalent) for lineage, control mapping, and operating model workflows.

Main tasks and responsibilities

• Run discovery workshops to confirm scope by legal entity, regulatory asks, priority datasets, and key stakeholders.
• Build and maintain CDE inventory: definitions, ownership, criticality, and mapping to reports/processes.
• Support SOR / Authoritative Source allocation: document authoritative sources, data contracts, and key dependencies.
• Define and maintain controls mapping: control points on SOR, AR, and endpoints; thresholds aligned to risk appetite; evidence requirements.
• Support data lineage creation/validation: source-to-endpoint traceability, transformation logic validation, and coverage reporting.
• Define and embed the operating model: exception workflows, DCRM lifecycle, triage routines, governance reporting, and closure evidence.
• Perform data profiling and reconciliation checks to support control design and validation readiness.
• Lead/support UAT and validation activities; coordinate defect triage and ensure sign-off evidence is complete.
• Produce an audit-ready documentation pack: lineage evidence, controls evidence, test packs, decision logs, and explainable outcomes.
• Track and communicate risks, dependencies, and changes impacting regulatory delivery outcomes through governance forums.

• Minimum 5 years of relevant experience in data governance, data quality, reporting controls, or data transformation programs (preferably in financial services / Capital Markets).
• Proven experience delivering governance-led programs involving CDEs, SOR/authoritative sources, controls, and lineage.
• Experience working in regulated remediation / regulatory delivery environments with exposure to validation, audit evidence, and structured governance.
• Strong stakeholder management across business, operations, technology, and governance functions.

Domain Knowledge

• Strong understanding of Capital Markets and Finance data domains (front-to-back awareness is a plus).
• Familiarity with risk appetite concepts as applied to data quality thresholds and control exceptions.

Technical / Analytical Skills

• Proficiency in SQL (advanced querying, reconciliation logic, data validation).
• Strong proficiency in Python for data analysis and automation (pandas, validation frameworks, scripting).
• Experience supporting or validating ETL/ELT pipelines and data quality frameworks (rules, thresholds, exception handling).
• Exposure to lineage and metadata approaches; ability to validate transformations and trace data across platforms.

Tooling / Delivery Methods

• Working knowledge of scheduling/orchestration tools such as Autosys and/or Apache Airflow (monitoring schedules, reruns, failure triage).
• Experience with CI/CD and release controls (Git, Harness, UrbanCode Deploy (UCD), Red Hat OpenShift or equivalent).
• Familiarity with large-scale storage patterns (e.g., AWS S3) for dataset movement and controls.
• Experience supporting BI/reporting outputs such as Tableau dashboards (data validation, extract refresh checks, reconciliation to source).

Nice-to-Have

• Experience with tools such as PySpark, Spark SQL, Hive, Impala, HDFS, Parquet, and Oracle databases.
• Hands-on exposure to DCRM tooling and operational exception management processes.
• Experience with governance/catalog tools and lineage documentation methods (Collibra/Alation/Informatica EDC/Purview or similar).
• Experience running delivery routines across workstreams (intake, triage, prioritization, wave planning, reporting).
• Experience working in Agile/Scrum delivery models.
• Familiarity with Visio (or equivalent) for lineage, control mapping, and operating model workflows.