Role Description
- The Technical Data Analyst will support a data transformation and controls program for a top-tier banking client Translating business requirements into technical specifications.
- Responsible for analyzing, validating, and reconciling large-scale datasets used for operational and regulatory reporting.
- Work across IT teams to define data requirements, quality rules, and controls for critical data elements. Facilitating the UK issue remediation process by hosting and facilitating conference calls amongst SMEs required to drive items to closure
- Tracking, adjudication, and reporting of Data Quality issues
- You will work with team members on topics including data profiling, lineage validation, source-to-target mapping, and root-cause analysis for data issues.
- Produce clear documentation and evidence to support UAT sign-off, audit reviews, and control attestations.
- Ensure traceability from data requirement → transformation logic → quality checks → reporting outputs → audit evidence.
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
- Minimum 5 years of relevant experience in data analytics, data quality, reporting controls, or data transformation programs (preferably in financial services).
- Proficiency in SQL (advanced querying, performance tuning, reconciliation logic).
- Strong proficiency in Python for data analysis and automation (pandas, data validation frameworks, scripting).
- Experience supporting or validating ETL/ELT pipelines and data quality frameworks (rules, thresholds, exception handling).
- Working knowledge of Autosys & Apache Airflow (monitoring schedules, reruns, failure triage).
- Experience with CI/CD tools (Git, Harness, UrbanCode Deploy (UCD), Red Hat OpenShift).
- 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
- Ideally, you have experience with tools including PySpark, Spark SQL, Hive, Impala, HDFS, Parquet, and Oracle databases.
- Experience in regulated or enterprise data environments (investment banking, risk, finance, compliance).
- Exposure to data governance concepts: critical data elements (CDEs), lineage, data quality dimensions, audit frameworks.
- Experience working in Agile/Scrum delivery models
- Knowledge of monitoring/alerting tools for data pipelines and batch operations.
- Familiarity with regulatory reporting domains or control frameworks (data controls, attestations, audit readiness).
- Familiarity with Visio, as well as DCRM tools to manage, document and monitor data quality issues
Main tasks and responsibilities
- Conduct discovery sessions to understand reporting objectives, key datasets, and edge cases.
- Perform data profiling and produce findings on completeness, accuracy, consistency, and timeliness.
- Design and execute reconciliation checks (source vs target, pre vs post transformation, report vs underlying data).
- Support creation of data requirements artifacts (data dictionaries, source-to-target mapping support, business rules, quality rules).
- Define and maintain data quality checks, exception logs, and analysis on recurring defects.
- Lead/Support UAT activities for reporting and controls; validate outcomes and document evidence.
- Produce audit-ready documentation: test packs, control evidence, runbooks, and explainable results.
- Partner with engineers to troubleshoot data issues and ensure analytical intent is preserved in implementation.
- Track and communicate risks, dependencies, and changes impacting reporting and control outcomes.
