Apprentice / Junior Data Engineer in Glasgow

We are seeking a motivated and intellectually curious Apprentice / Junior Data Engineer to join our growing Data & Analytics team. This is a unique opportunity for a candidate at the beginning of their data engineering journey to work alongside seasoned architects and engineers, gaining hands‑on exposure to real‑world data ecosystems – from ingestion pipelines to data lake environments – while contributing meaningfully from day one.

The ideal candidate brings foundational knowledge of data architecture principles, demonstrable enthusiasm for data migration and pipeline work, and the drive to grow into a full‑fledged data engineering practitioner.

Key Responsibilities

• Assist in the design, development, and maintenance of data pipelines for ingestion, transformation, and storage across structured and unstructured data sources.
• Support end‑to‑end data migration initiatives – including data profiling, mapping, cleansing, validation, and post‑migration reconciliation.
• Work within data lake and data warehouse environments, contributing to data cataloguing, partitioning strategies, and storage layer optimisation.
• Collaborate with data architects, senior engineers and relevant stakeholders to implement scalable, reliable, and well‑documented data solutions.
• Participate in data quality monitoring, identifying anomalies and assisting in root cause analysis.
• Write, test, and optimise SQL queries and basic ETL/ELT scripts for data processing tasks.
• Contribute to documentation of data flows, schemas, lineage, and migration runbooks.
• Engage actively in sprint ceremonies, technical reviews, and knowledge‑sharing sessions.

Must-Have Skills & Knowledge

Data Migration

• Hands‑on experience executing or assisting in data migration projects (on‑premises to cloud, legacy to modern platforms, or system‑to‑system).
• Practical knowledge of migration phases: discovery, data profiling, mapping specifications, ETL build, testing, cutover, and validation.
• Ability to write SQL for data comparison, reconciliation counts, and pre/post‑migration validation checks.
• Familiarity with data mapping documents and migration runbooks.

Data Lake & Architecture Fundamentals

• Basic understanding of data lake concepts – storage layers (raw / curated / consumption), zone structures, and file format awareness (Parquet, Avro, JSON, CSV).
• Awareness of data architecture patterns such as Lambda or Kappa architecture, medallion architecture (Bronze / Silver / Gold), and batch vs. streaming trade‑offs.
• Understanding of the distinction between data lakes, data warehouses, and lakehouses.

Core Technical Competencies

• Proficiency in SQL – including joins, aggregations, window functions, and query optimisation basics.
• Working knowledge of at least one programming language relevant to data engineering: Python, Scala, or PySpark.
• Familiarity with at least one major cloud platform (AWS, Azure, or GCP) and its core data services (e.g., S3/ADLS, Glue/ADF, Redshift/Synapse/BigQuery).
• Understanding of version control practices using Git.

Good-to-Have Skills & Knowledge

Tools & Platforms

• Exposure to orchestration tools such as AWS Glue workflows or similar tools.
• Experience with Databricks or similar tools for distributed data processing.
• Familiarity with data cataloguing and governance tools (e.g., Apache Atlas, AWS Glue Data Catalog, Microsoft Purview etc.).
• Basic knowledge of streaming technologies.
• Experience with dbt (data build tool) for transformation layer development.

Practices & Methodologies

• Awareness of DataOps principles and CI/CD practices applied to data pipelines.
• Exposure to Agile / Scrum delivery methodologies in a data or technology context.
• Understanding of data governance fundamentals – data lineage, metadata management, and PII handling.
• Familiarity with Infrastructure-as-Code tools for Cloud resource provisioning.

Domain & Soft Skills

• Exposure to public sector or regulated industry data environments is a distinct advantage.
• Strong analytical thinking and structured problem‑solving approach.
• Clear written and verbal communication – able to articulate technical concepts to non‑technical stakeholders.
• Intellectual curiosity, a growth mindset, and the initiative to self‑learn emerging data technologies.

We are an equal opportunities employer. We welcome applications from candidates of all backgrounds, experiences, and identities. If you require any adjustments during the recruitment process, please do not hesitate to let us know.