Data Quality & Governance Engineer

We are hiring a hands-on Data Quality & Governance Engineer to build and run the governance that underpins our Data & AI function.

You will sit at the intersection of engineering, governance, and risk. Your focus is to build quality, metadata, lineage, and control capabilities into our data and AI platforms, and fix issues where data is created. The goal: reduce downstream issues and give LHV a clear, single view of how critical data and AI assets are governed.

You will work closely with the Head of Data & AI, engineers, AI/ML team, risk, and technology partners to turn policy into automated patterns across the platform.

 What You Will Do

Data Quality and Observability by Design:

• Implement automated data quality checks, alerts, and observability for critical datasets, data products, and AI features (Python, SQL, dbt, similar tools).
• Work with source system teams to identify root causes and fix data at source.
• Embed controls in pipelines and transformations (schema, referential integrity, freshness, PII, data contracts).
• Define and monitor data SLIs/SLOs (availability, latency, freshness, completeness) and feed these back to teams.
• Integrate quality and observability signals into monitoring and incident tooling (e.g. Coralogix, Jira)

Build and Run Governance Operations:

• Help design and operate the Data & AI governance “spine” that connects policies, data, AI, and risk.
• Implement and maintain:
  • An enterprise data catalogue and glossary
  • End‑to‑end lineage from source to warehouse/lake to products, models, and reports
  • Ownership, stewardship, and criticality metadata aligned to the Data & AI Stewardship Model
• Ensure governance tooling reflects how data actually flows.
• Link governed assets to policies, controls, and risks, giving a single view of what data we have, where it comes from, how it is used, and who is accountable.

Enable Safe, Governed AI and Analytics:

• Make core data assets “AI ready” through strong provenance, documentation, and quality standards.
• Integrate governance with AI services and model lifecycle tooling so:
  • Models and AI use cases are registered, owned, and linked to their data
  • Training data, features, and outputs have clear lineage back to source
  • Data quality and model monitoring (performance, drift, bias) can be traced end to end
• Partner with Data Scientists, ML Engineers, and Analysts to design repeatable, governed patterns for feature stores, training datasets, and AI outputs.

Support Stewardship, Risk and Compliance:

• Work with Data Owners, Stewards, Custodians, and AI Stewards to:
  • Identify and prioritise Critical Data Elements (CDEs) and high‑impact AI use cases
  • Capture ownership, definitions, quality rules, and control requirements
• Implement practical solutions for:
  • Classification, retention, and access control (e.g. GDPR, banking regulation)
  • Audit trails and evidence for key data flows and AI services
• Provide clear views of where critical data comes from, how it flows, and how well it is controlled, including:
  • Lineage, quality coverage, and control effectiveness
  • Inputs to the Data & AI Governance Forum
  • Regulatory and internal assurance materials

Technical guidance:

• Provide technical guidance and code review for governance and quality patterns.
• Contribute to shared practices for governance as code, CI/CD, testing, and observability for data and AI workloads.
• Work in an agile environment with product, engineering, risk, and business stakeholders to turn governance requirements into deliverable work.
• Help evaluate and introduce governance and observability tooling (catalogues, lineage, data observability, AI governance).

 What We Are Looking For

Essential:

• Significant experience within in a data‑focused engineering role (e.g. Data Engineer, Analytics Engineer, Data Platform Engineer, Governance Engineer) with strong emphasis on data quality, governance, or observability.
• Experience with financial data or in financial services or other regulated industries.
• Strong hands-on Python and SQL for checks, automation, and integrations.
• Proven experience implementing automated data quality rules and monitoring (dbt tests, custom SQL/Python checks, data observability tools) and using them to drive fixes at source.
• Practical understanding of data governance (catalogue, lineage, ownership/stewardship, CDEs, access control, retention) and how to operationalise it in production.
• Experience with cloud‑native data platforms, ideally AWS (e.g. Redshift, S3, Glue, Lambda, IAM) or similar.
• Familiarity with modern data modelling and transformation (e.g. dbt, dimensional modelling, star/snowflake schemas).
• Experience with CI/CD and infrastructure as code for data systems (e.g. GitHub Actions, CodeBuild/CodePipeline, Terraform).

Desirable:

• Experience with data governance platforms or catalogues (e.g. Collibra, Atlan, Collate, Alation, OpenMetadata).
• Experience with data observability or monitoring tooling (e.g. Monte Carlo, Coralogix, custom metrics/alerting).
• Exposure to ML/AI pipelines, model registries, feature stores, and monitoring (e.g. MLflow, Evidently).
• Experience defining and operating data products with SLAs, contracts, and clear ownership.