Data Validation Specialist

About the role

Addepar is seeking a Data Validation Specialist to join their growing team in Edinburgh, UK. This role sits at the intersection of data engineering and financial technology, focusing on ensuring that every dataset flowing through the platform meets the highest standards of accuracy and completeness. The specialist will partner with data engineers, analysts, and product teams to build systematic checks that catch errors before they impact downstream consumers. You will take ownership of validation logic that underpins reliable reporting and decision-making for wealth management professionals who depend on Addepar's platform daily.

Key facts

What you'll do

• Design and implement automated data validation frameworks to catch errors in financial datasets before they reach production systems and end users.
• Collaborate with cross-functional teams including data engineers, analysts, and product managers to define data quality standards, metrics, and acceptance criteria for each release.
• Monitor data pipelines on a continuous basis and perform routine quality checks to identify anomalies, inconsistencies, or missing values in incoming and stored datasets.
• Document all validation procedures in detail and create clear runbooks that describe how each check operates and what specific actions to take when issues are detected.
• Investigate data discrepancies reported by internal stakeholders, trace them back to their root cause in upstream source systems, and propose lasting fixes.
• Develop and maintain comprehensive test datasets that cover edge cases, boundary conditions, and known failure modes for critical financial calculations and aggregations.
• Participate actively in code reviews for data transformation logic to ensure that validation rules are correctly applied and consistently enforced throughout the entire pipeline.
• Build interactive dashboards and scheduled reports that visualize data quality trends over time, giving leadership and engineering teams clear visibility into platform health.
• Work directly with the Edinburgh-based team to refine and extend validation logic as new data sources are onboarded and existing schemas evolve over time.
• Support the release process by running pre-deployment validation suites, reviewing results, and formally signing off on data readiness before each production deployment proceeds.
• Coordinate with external data providers to validate incoming feeds and ensure contractual data quality obligations are met on an ongoing basis.
• Lead periodic data quality audits that assess the overall health of the platform's datasets and recommend improvements to validation coverage and detection capabilities.

Requirements

• Bachelor's degree in a quantitative field such as mathematics, statistics, computer science, or a related discipline from an accredited institution.
• Demonstrated experience working with large financial or transactional datasets in a professional environment, with a track record of improving data quality.
• Strong attention to detail and a methodical, systematic approach to identifying, categorizing, and resolving data quality issues of varying complexity.
• Proficiency in at least one programming language commonly used for data work such as Python or SQL, with the ability to write clean, maintainable code.
• Solid familiarity with data validation concepts including schema enforcement, referential integrity, range checks, and uniqueness constraints across relational datasets.
• Ability to communicate technical findings and recommendations clearly to both technical peers and non-technical stakeholders in a concise and actionable manner.
• Experience working in a collaborative team setting with regular cross-department interaction and a willingness to share knowledge with colleagues.
• Willingness to learn the specifics of Addepar's wealth management data model, platform architecture, and the unique requirements of the financial services domain.

Nice to have

• Previous experience in the financial services or wealth management industry, with an understanding of the data challenges specific to that sector.
• Knowledge of data validation tools or frameworks beyond standard SQL and Python libraries, including specialized quality assurance platforms.
• Exposure to cloud-based data platforms such as AWS, GCP, or Azure, particularly in the context of building and maintaining data pipelines.
• Understanding of regulatory requirements related to financial data accuracy, retention, and reporting that may apply to wealth management technology platforms.
• Experience with data governance frameworks and data cataloging tools that help organizations maintain visibility and control over their data assets.

Skills & tools

• Python for scripting validation logic, building automated checks, and writing utility scripts that streamline the data quality workflow.
• SQL for querying and validating data across relational databases, including writing complex joins and aggregations to verify data integrity.
• Data pipeline tools and workflow orchestration platforms that manage the movement and transformation of data across systems.
• Version control systems such as Git for managing validation code, tracking changes, and supporting collaborative development practices.
• Data visualization libraries and tools for building quality monitoring dashboards that make trends and anomalies easy to spot.
• Spreadsheet tools for ad hoc data analysis, quick spot checks, and communicating findings to stakeholders in accessible formats.

Practical notes

• This role is based in Addepar's Edinburgh office, with a hybrid or on-site work arrangement as determined by the team and company policy.
• The hiring process includes technical interviews focused on data validation concepts, SQL problem-solving, and real-world data scenario walkthroughs.
• Candidates should be prepared to discuss past projects where they improved data quality, caught significant data errors, or built validation systems from the ground up.
• Addepar values candidates who can work independently on complex problems while also contributing to a supportive and collaborative team environment in Edinburgh.
• The team values diversity of thought and background, and encourages applicants from a wide range of academic and professional disciplines to apply.