Data Engineer

Role purpose

Key responsibilities

• Design and maintain pipelines to ingest, clean and normalise structured and unstructured data.
• Build logic and confidence-scoring frameworks to produce trusted, conflict-free data records.
• Clean and validate data for accuracy, consistency and uniformity across sources.
• Perform exploratory data analysis and visualise trends and anomalies to identify paths to AI-ready data.
• Collaborate with AI engineers and ML operations teams to deliver clean, well-attributed data to downstream models.
• Analyse AI agent usage across the business.

Skills and experience

• Core skills include data wrangling, cleaning and normalisation, validation, feature engineering when needed, entity resolution, deduplication, exploratory analysis, and data quality assessment and monitoring.
• Python proficiency is required.
• Data visualisation includes clear charts and graphs, visual and summary presentations, dashboards and reporting outputs.
• Clear technical documentation and communication of technical findings to non-technical audiences are required.
• The role calls for collaboration, structured problem solving and willingness to ask questions and learn from feedback.
• Nice to have: exposure to NLP or text classification; familiarity with graph and semantic layers; cloud data platform experience; understanding of AI data governance.

Technologies

Hours and process

• Working hours are 9:00 am–5:30 pm, Monday to Friday.
• The interview process has two stages.

Workplace and contract

• The role is based in Birmingham and uses hybrid working, combining office and home working.
• The role is permanent.

Salary package

• The salary package is £26,227.50, plus company benefits, a flexible benefits scheme and two paid volunteering days per year.

About SCC

Requirements

• Proficiency in Python.
• Skills in data wrangling, cleaning and normalisation for structured and unstructured data.
• Experience with data validation, feature engineering when needed, entity resolution and deduplication.
• Ability to perform exploratory data analysis and assess and monitor data quality.
• Ability to design clear, insight-driven charts and graphs, present data in visual and summary formats, and build dashboards or reporting outputs.
• Ability to document technical work clearly and communicate findings to non-technical audiences.
• Collaborative, structured and methodical approach to problem solving, with willingness to ask questions and learn from feedback.
• Exposure to NLP or text classification is a nice-to-have.
• Familiarity with graph and semantic layers is a nice-to-have.
• Experience with cloud data platforms is a nice-to-have.
• Understanding of AI data governance principles is a nice-to-have.