Data Engineer

Strategy & Stakeholder Management

· Collaborate with Data Scientists, Digital Forensics, Technology, investigation and audit teams to align data products to engagement needs.

· Partnering with auditors, investigators, data analyst and digital forensics specialists.

· Support governance tied to tech development and strategy (cataloguing, metadata, stewardship).

Tactical & Operational

· Develop scalable ETL/ELT workflows using Azure Data Factory, Synapse/Spark, Databricks, SQL, and Python.

· Creating well-structured, audit-ready datasets and curated layers for reuse across engagements.

· Implementing data quality controls, validation rules, lineage tracking, and transformation logic.

· Uphold privacy, access control, evidence integrity.

· Develop CI/CD, testing, observability and rollback for data services

· Support ML operationalization within A&I and explainability.

Teams & Capability Development

· Assist packaging of reusable components.

· Demonstrate reliable operations and incident response

· Participate in learning and experience transfer.

Ways of Working

· Uphold A&I/DEX methodologies

· Promote analytics and automation for digital auditing and investigations

· Drive adherence to data product SLAs/SLOs and continuous improvement.

· Operate in line with A&I independence and objectivity principles when designing and delivering data solutions.

· Effectively communicate technology, infrastructure, and deployment choices to both technical and non-technical stakeholders.

· Accountable for ensuring data products meet agreed quality, reliability, and audit/investigation‑readiness standards.

CORE SKILLS

· Experience building data products for analytics, ML and forensic workloads

· Strong proficiency in SQL, Python, Spark, and data orchestration (ADF/Airflow).

· Comfort with ambiguity and iterative delivery.

· Knowledge of data privacy, secure handling and auditability.

· Azure/Spark/Synapse/ADF, SQL, Python; strong grasp of batch & streaming architectures; schema design and optimization.

· CI/CD (GitHub/Azure DevOps), testing frameworks, observability (lineage, metrics, alerting), incident response.

· IAM, RBAC/ABAC, encryption, PII handling, auditability and evidence integrity; comfort in regulated environments.

· Strong data modelling skills (analytical and dimensional models) to support audit, investigation, and repeatable analytics use cases.

· Strong documentation skills covering data logic, transformations, assumptions, and limitations for audit transparency