Technical Analyst – Data Governance, Controls & Traceability

• The Technical Analyst will support a Data Governance & Controls remediation program for a top-tier banking client, aligned to an ECB Remediation operating model.
• Support remediation stand-up and ways of working (pods, intake, tooling), helping establish delivery routines, governance, and reporting.
• Responsible for translating regulatory and governance policies (GDPR, CCPA, HIPAA, Basel, internal security standards) into clear technical requirements and control specifications.
• Work with Legal, Compliance, Security, Data Platform, and IT teams to define control requirements, maintain remediation backlogs, and track closure.
• Contribute to and maintain the controls gap assessment, including prioritization, ownership tracking, and closure evidence.
• Identify data sourcing gaps, coordinate root-cause analysis with SMEs, and document and track the agreed closure plan.
• Support delivery of manual controls automation waves, ensuring requirements, testing, sign-offs, and operational readiness are complete.
• Ensure catalogue lineage is populated and traceability coverage is measurable and reported for priority data domains and reporting chains.
• Engage in process mapping, control specification (decision tables, data contracts, rules), and validating outcomes through UAT and audit documentation.
• Participate from discovery workshops through implementation and continuous improvement.

Role Description

• The Technical Analyst will support a Data Governance & Controls remediation program for a top-tier banking client, aligned to an ECB Remediation operating model.
• Support remediation stand-up and ways of working (pods, intake, tooling), helping establish delivery routines, governance, and reporting.
• Responsible for translating regulatory and governance policies (GDPR, CCPA, HIPAA, Basel, internal security standards) into clear technical requirements and control specifications.
• Work with Legal, Compliance, Security, Data Platform, and IT teams to define control requirements, maintain remediation backlogs, and track closure.
• Contribute to and maintain the controls gap assessment, including prioritization, ownership tracking, and closure evidence.
• Identify data sourcing gaps, coordinate root-cause analysis with SMEs, and document and track the agreed closure plan.
• Support delivery of manual controls automation waves, ensuring requirements, testing, sign-offs, and operational readiness are complete.
• Ensure catalogue lineage is populated and traceability coverage is measurable and reported for priority data domains and reporting chains.
• Engage in process mapping, control specification (decision tables, data contracts, rules), and validating outcomes through UAT and audit documentation.
• Participate from discovery workshops through implementation and continuous improvement.
• Ensure traceability from policy → control → implementation → test evidence → audit-ready documentation pack.

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 we do NOT offer visa sponsorship.

Core Skills & Experience

• Minimum 3 years of relevant experience in data governance, compliance, controls, remediation, or automation programs (preferably in financial services).
• Strong proficiency in Python for data engineering and automation.
• 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.
• Experience designing and supporting ETL/ELT pipelines and data quality frameworks (rules, thresholds, exception handling).
• Working knowledge of Autosys and 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 AWS S3 for large-scale data storage and dataset movement patterns.
• Experience supporting Tableau dashboards (data validation, extract refresh checks, reconciliation to source).

Nice-to-Have

• Experience in regulated or enterprise data environments (banking, compliance).
• Exposure to data governance, lineage, controls frameworks, and audit evidence standards.
• Experience working in factory-style remediation models (intake, triage, prioritization, wave planning, pod execution, reporting).
• Experience working in Agile/Scrum delivery models.
• Knowledge of monitoring and alerting tools for data pipelines and batch operations.

Main Tasks and Responsibilities

• Support remediation stand-up: pods structure, intake process, tooling setup, and delivery routines.
• Conduct discovery workshops to define scope, policies, control expectations, and remediation edge cases.
• Drive delivery of quick-win remediation waves, including triage, tracking, and closure evidence capture.
• Produce control specifications and delivery artefacts (decision tables, data contracts, user stories, acceptance criteria, test cases).
• Maintain remediation backlogs (policy/control requirements) and track lifecycle changes, ownership, and closures.
• Support delivery and reporting of the controls gap assessment (prioritization, dependencies, closure tracking).
• Identify data sourcing gaps, coordinate SME working sessions, and document and track agreed closure plans.
• Support manual controls automation waves by validating requirements, coordinating testing, and ensuring sign-off readiness.
• Ensure catalogue lineage is populated and traceability coverage is measurable and reported.
• Lead/support UAT for codified controls; validate automation outcomes and manage defect triage.
• Produce an audit-ready documentation pack including decision logs, test packs, control mappings, lineage/traceability evidence, and approvals.
• Partner with developers to ensure policy intent is preserved in implementation and outcomes are explainable.

#J-18808-Ljbffr