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Senior Data Engineer
Senior Data Engineer
Location: Maidenhead, United Kingdom Position Type: Full-time Experience Level: 5+ years
Role Summary
We are looking for an experienced Senior Data Engineer to design, build, optimise, and maintain scalable data platforms and data pipelines across modern cloud and enterprise environments.
The role involves:
- Collaborating with architects, analysts, data scientists, product owners, and client stakeholders to deliver robust, secure, and high-performing data solutions.
- Taking ownership of complex data engineering workstreams.
- Providing technical leadership to junior engineers.
- Ensuring solutions meet architecture, security, governance, and operational standards.
This is a senior delivery role requiring a mix of technical depth and practical delivery experience, particularly in complex, regulated, or enterprise environments.
Key Responsibilities
Data Engineering and Platform Delivery
- Design, develop, test, deploy, and maintain scalable data pipelines using modern cloud-native and enterprise tools.
- Build robust ETL/ELT processes for ingesting, transforming, validating, and publishing data from structured and unstructured sources.
- Work with batch, near-real-time, and streaming data processing patterns, if required.
- Develop reusable data engineering frameworks, templates, and automation scripts.
- Support data lakes, lakehouses, data warehouses, operational data stores, and analytics platforms.
- Optimise pipelines for performance, cost, reliability, scalability, and maintainability.
- Ensure data solutions are production-ready, monitored, and documented.
Cloud and Technology Implementation
- Build data solutions on cloud platforms (Microsoft Azure, AWS, or Google Cloud), with a preference for Azure.
- Use tools such as Azure Data Factory, Synapse Analytics, Databricks, Fabric, AWS Glue, Pandas, Snowflake, dbt, Airflow, Kafka, or equivalents.
- Implement data ingestion from APIs, databases, files, SaaS platforms, event streams, and third-party systems.
- Apply infrastructure-as-code (IaC), CI/CD pipelines, and automated deployments.
- Collaborate with DevOps and platform teams for secure, reliable deployments.
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Data Modelling, Quality, and Governance
- Design analytical data models (dimensional, data vault, or star schemas) and curated datasets.
- Apply data quality rules, validation checks, reconciliation controls, and exception handling.
- Support metadata management, lineage, and data catalogue requirements.
- Ensure compliance with security, privacy, access control, retention, and audit policies.
- Work with stakeholders to define data mappings, transformation logic, and acceptance criteria.
Technical Leadership
- Lead data engineering workstreams, from discovery to design, build, and support transition.
- Provide technical guidance, mentoring, and code reviews for junior and mid-level engineers.
- Translate high-level architecture into practical engineering deliverables.
- Contribute to technical decision-making, estimation, and risk management.
- Identify risks, dependencies, blockers, and improvement opportunities.
- Promote engineering standards, documentation, and best practices.
Stakeholder and Delivery Management
- Collaborate with product owners, analysts, architects, testers, data analysts, and client stakeholders.
- Participate in agile ceremonies (sprint planning, stand-ups, reviews, retroPROs).
- Support requirements analysis, technical sessions, and demonstrations.
- Produce technical documentation, data flow diagrams, and deployment guides.
- Ensure knowledge transfer, monitoring, incident response, and handover to support teams.
Experience and Skills
Essential Requirements
Technical Skills
- 5+ years of experience as a Data Engineer or Senior Data Engineer (enterprise or cloud environments).
- Advanced SQL (optimisation, stored procedures, data modelling).
- Python or PySpark (data processing, automation, transformations).
- Experience with ETL/ELT tools (AWS Glue, Azure Data Factory, Databricks, dbt, Airflow, Synapse).
- Azure expertise (preferably Microsoft Azure) or equivalent experience.
- Knowledge of data lake, lakehouse, warehouse, and analytical platforms.
- Understanding of batch processing, incremental loads, CDC, API ingestion, and file-based patterns.
- Data validation, reconciliation, and quality checks expertise.
- Use of Git-based source control and CI/CD pipelines.
- Security, access control, encryption, and data privacy knowledge.
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Delivery Experience
- Production-grade data platform or pipeline delivery experience.
- Full-spectrum delivery (requirements, design, build, test, release, and support).
- Agile delivery team experience.
- Technical documentation and stakeholder communication.
- Experience leading teams or mentoring engineers.
- Problem-solving, troubleshooting, and prioritisation skills.
- Ability to work independently with strong ownership mindset.
Desirable Qualifications
- Azure certifications (e.g., Azure Data Engineer Associate).
- Azure-specific tools (Fabric, Synapse, Data Lake Storage, Azure Purview).
- Databricks, Delta Lake, MLflow (lakehouse patterns).
- Snowflake, Redshift, BigQuery (cloud data warehouses).
- Streaming technologies (Kafka, Event Hubs).
- Data governance and lineage experience.
- IaC tools (Terraform, Bicep, Docker, Kubernetes).
Behavioural Competencies
- Strong ownership mindset.
- Clear and confident communicator (across teams).
- Pragmatic problem solver, balancing engineering and delivery timelines.
- Collaborative team player who contributes to shared goals.
- Detail-oriented and reliability focused.
- Adaptable to fast-paced, multi-disciplinary environments.
- Constructive challenge seeker.
- Continuous learning mindset.
Typical Deliverables
- ETL/ELT pipelines, data ingestion/processing, and validation components.
- Data models, schemas, transformation specifications, and mapping documents.
- CI/CD pipelines and environment configurations.
- Automated data quality checks and reconciliation reports.
- Technical design documentation and data flow diagrams.
- Knowledge transfer sessions, runbooks, and support documentation.
- Optimisation recommendations and improvements.
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Skills
SQL
Python
PySpark
Azure Data Factory
Databricks
ETL/ELT
Data Modelling
CI/CD
Azure
Spark
dbt
Airflow
Data Lakehouse
Git
API Ingestion
Technical Leadership
Location