Data Engineer

Duration: until 07/05/2027

Location: Sheffield, hybrid 60% office 40% home

You will design and implement robust ETL/ELT pipelines, develop data models for management information (MI) reporting, and create dashboards that provide actionable insights for operational, risk, and compliance stakeholders.

Key Responsibilities

Design, build, and maintain scalable ETL/ELT pipelines to ingest and process log and event data from AI platform components.

Develop and optimize data models for aggregation, reporting, and analytics to support MI and governance requirements.

Collaborate with platform, DevOps, and security teams to ensure comprehensive data capture for monitoring and controls.

Create and maintain dashboards and visualizations using tools such as Power BI, Tableau, Kibana, or Grafana.

Write efficient SQL, Python, or Spark code for data extraction, transformation, and analysis.

Implement data quality checks and validation processes to ensure accuracy and reliability of monitoring data.

Support automated alerting and reporting for operational incidents, policy violations, and compliance metrics.

Document data flows, models, and dashboard logic for transparency and auditability.

Participate in requirements gathering with business, risk, and compliance teams to ensure MI meets governance standards.

Continuously improve data pipelines and analytics solutions for performance, scalability, and usability.

Required Skills & Qualifications

5+ years of experience in data engineering, analytics engineering, or a related field.

Strong experience designing and implementing ETL/ELT pipelines for large-scale log or event data.

Proficiency in data modelling, aggregation, and building MI/reporting solutions.

Hands-on experience with dashboarding and visualization tools (Power BI, Tableau, Kibana, Grafana, or similar).

Advanced SQL skills and experience with Python or Spark for data processing and analytics.

Familiarity with cloud data platforms (Azure, AWS, GCP) and modern data architectures.

Experience working with monitoring, observability, or security log data is a strong plus.

Understanding of data governance, privacy, and compliance requirements in enterprise environments.

Excellent problem-solving, communication, and documentation skills.

Ability to work collaboratively in a cross-functional, agile team environment.

Preferred Qualifications

Experience in regulated industries (eg, banking, insurance, healthcare).

Knowledge of AI/ML platform monitoring and controls.

Experience with log aggregation tools (ELK Stack, Splunk, etc.).

Familiarity with operational risk, compliance, or audit reporting.