“Techno-Functional” Analyst– Capital Markets Data Transformation

Boundaryless; Greater London; Hybrid; GBP 60,000 - 80,000; Full time

Job summary

Boundaryless is seeking a Techno-Functional Business Analyst to support a data transformation program for a banking client. The role involves driving remediation efforts, data quality checks, and documentation processes, requiring at least 5 years of experience, particularly in Capital Markets.

Applicants should possess strong SQL and Python skills and be prepared for on-site work in London (Canary Wharf) at least three days per week. The company emphasizes hands-on data analysis and control remediation.

Qualifications

• Minimum 5 years of relevant experience in data analytics or transformation.
• Experience in regulated remediation environments with audit exposure.
• Strong understanding of Capital Markets data and reporting expectations.

Responsibilities

• Support remediation stand-up and governance rhythms.
• Conduct discovery sessions to understand reporting objectives.
• Drive quick-win remediation waves, ensuring closure evidence.

Skills

Data analytics

Data quality

SQL

Python

ETL/ELT pipelines

Reporting controls

Agile/Scrum

Tools

Tableau

Apache Airflow

Autosys

AWS S3

Job description

Role Description

• The Techno-Functional Business Analyst will support a data transformation and controls remediation program for a top-tier banking client, aligned to an ECB Remediation approach.
• Support the stand-up and operating model of the remediation (pods structure, intake process, tooling, delivery routines).
• Drive delivery of quick-win remediation waves, ensuring requirements, triage, prioritization, and closure tracking across stakeholders.
• Contribute to and maintain the controls gap assessment, supporting prioritization and ensuring closure plans are realistic and tracked.
• Identify data sourcing gaps, coordinate root-cause discussions, and support agreement of closure plans with business and technology teams.
• Support delivery of manual controls automation waves, ensuring requirements, testing, evidence capture, and operational readiness.
• Ensure catalogue lineage is populated, with measurable traceability coverage across critical data elements and reporting chains.
• Produce clear documentation and evidence to support UAT sign-off, audit reviews, and control attestations.
• Ensure traceability from requirement → transformation logic → quality checks → reporting outputs → audit evidence, aligned to remediation governance.

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.

Experience Requirements & Qualifications

• Minimum 5 years of relevant experience in data analytics, data quality, reporting controls, or data transformation programs (preferably in financial services / Capital Markets).
• Experience working in regulated remediation environments, with exposure to audit evidence, controls testing, and structured governance.
• Strong domain understanding of Capital Markets data and reporting expectations (front-to-back awareness is a plus).
• Proficiency in SQL (advanced querying, performance tuning, reconciliation logic).
• 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).
• 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.
• Exposure to data governance concepts: critical data elements (CDEs), lineage, data quality dimensions, audit frameworks.
• Experience running delivery (intake, triage, prioritization, wave planning, pod execution, and reporting).
• Experience working in Agile/Scrum delivery models.
• Knowledge of monitoring/alerting tools for data pipelines and batch operations.
• Familiarity with control frameworks (data controls, attestations, audit readiness) and issue management tooling (e.g., DCRM).
• Familiarity with Visio or equivalent for process, lineage, and control mapping documentation.

Main tasks and responsibilities

• Support remediation stand-up: pods model, intake process, tooling usage, delivery routines, and governance rhythms.
• Conduct discovery sessions to understand reporting objectives, key datasets, control expectations, and remediation edge cases.
• Drive delivery of quick-win remediation waves, ensuring triage, prioritization, and closure evidence are complete.
• Perform data profiling and produce findings on completeness, accuracy, consistency, and timeliness to support gap analysis.
• Design and execute reconciliation checks (source vs target, pre vs post transformation, report vs underlying data).
• Support delivery and maintenance of controls gap assessment outputs, including prioritization and closure tracking.
• Identify data sourcing gaps, coordinate root-cause analysis with SMEs, and document agreed closure plans.
• Support manual controls automation waves by defining requirements, validating outputs, and ensuring operational readiness.
• Ensure catalogue lineage is populated and traceability coverage is measurable and reported for key reporting chains.
• Lead/Support UAT activities for reporting and controls; validate outcomes and document evidence for sign-off.
• Produce an audit-ready documentation pack: test packs, control evidence, lineage evidence, decision logs, and explainable results.
• Track and communicate risks, dependencies, and changes impacting remediation outcomes, ensuring governance readiness.