Boundaryless Automation
Technical Analyst – Data Governance, Controls & Traceability
London
Posted about 8 hours ago
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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.
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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.
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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.
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Jessica, London
Skills
Data Governance
Python
PySpark
Spark SQL
Hive
Impala
HDFS
Parquet
SQL
Oracle
ETL/ELT
Autosys
Apache Airflow
CI/CD
AWS S3
Tableau
Location