Experienced Business Analyst (Data Transformation, Investment Banking) in Potters Bar

Boundaryless is a boutique consulting firm, providing clients with deep expertise in advanced analytics & data forensics, agentic automation, and Artificial Intelligence solutions. We operate around the world, with offices in 7 countries: United Kingdom, France, The Netherlands, Switzerland, India, Sri Lanka, and the USA. Boundaryless is an equal opportunity employer, striving for diversity with a team representing 13 nationalities and 50% female employees.

Due to the growing demand from financial services organisations for automation services supporting data transformation and agentic process automation, we have open positions for Business Analysts who understand operational and regulatory reporting processes in investment banking. Capital Markets knowledge and Finance experience are key components for this role.

The Technical Data Analyst will support a data transformation and controls programme for a top-tier banking client. Responsibilities include:

• Translating business requirements into technical specifications.
• Analyzing, validating, and reconciling large-scale datasets used for operational and regulatory reporting.
• Working 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.
• Working on topics including data profiling, lineage validation, source-to-target mapping, and root-cause analysis for data issues.
• Producing clear documentation and evidence to support UAT sign-off, audit reviews, and control attestations.
• Ensuring traceability from data requirement to transformation logic, quality checks, reporting outputs, and audit evidence.

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.

Experience Requirements & Qualifications

• Minimum 5 years of relevant experience in data analytics, data quality, reporting controls, or data transformation programmes (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

• 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

• Conducting discovery sessions to understand reporting objectives, key datasets, and edge cases.
• Performing data profiling and producing findings on completeness, accuracy, consistency, and timeliness.
• Designing and executing reconciliation checks (source vs target, pre vs post transformation, report vs underlying data).
• Supporting the creation of data requirements artifacts (data dictionaries, source-to-target mapping support, business rules, quality rules).
• Defining and maintaining data quality checks, exception logs, and analysis on recurring defects.
• Leading/supporting UAT activities for reporting and controls; validating outcomes and documenting evidence.
• Producing audit-ready documentation: test packs, control evidence, runbooks, and explainable results.
• Partnering with engineers to troubleshoot data issues and ensure analytical intent is preserved in implementation.
• Tracking and communicating risks, dependencies, and changes impacting reporting and control outcomes.

If you feel you fit this profile, please apply via email. Please send your CV as well as an overview of automation projects you have delivered.