Top Data Engineer Jobs in Bristol

Designs, builds, and operates Python-based data platforms, web applications, APIs, and agentic workflows. Develops batch and streaming pipelines, AWS data services, secure backend systems, CI/CD, monitoring, and governance controls. Translates business needs into production solutions, establishes reliable AI-agent safeguards, mentors engineers, reviews designs and code, and coaches business users building front ends with AI-assisted tools.

Designs, delivers, and optimizes scalable cloud-based data platforms and solutions. Responsibilities include building ingestion, transformation, modeling, and integration pipelines; ensuring performance, security, governance, reliability, and data quality; collaborating with analysts, scientists, architects, and project managers; supporting feature engineering and analytical projects; implementing monitoring, testing, CI/CD, documentation, and data lineage; and troubleshooting complex data and infrastructure issues.

Designs, develops, tests, deploys, and supports Databricks-based data products and scalable ETL/ELT pipelines. Uses PySpark, Spark SQL, SQL, Python, Azure, and Power BI to build curated datasets for reporting, analytics, and operational decision-making. Responsibilities include data quality, reconciliation, monitoring, optimization, documentation, governance, production support, and collaboration with architects, analysts, and stakeholders.

Develop and maintain customer-facing analytical reporting, data pipelines, and data models. Build Python applications, ensure data quality and consistency, collaborate with analytics engineers, and enable business teams to access actionable data. The role also involves cloud data warehouse optimization, infrastructure as code, software engineering best practices, and cross-functional product collaboration within an agile SaaS environment.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Build and deliver end-to-end data products on GCP, including data modelling, ETL/ELT, testing, validation, DevOps, governance, deployment, and go-live. Lead MDM strategy, architecture, product selection, integration, and implementation, including bespoke solutions and real-time data patterns. Provide senior technical leadership to multidisciplinary feature teams improving FDP data quality and consumption readiness.

Build and maintain data platforms, pipelines, lakes, warehouses, and integrated data models for analytics, reporting, data science, and AI solutions. The role involves hands-on client delivery, software engineering, data transformation, technical problem-solving, and advising clients. Engineers may lead teams or set technical direction and will occasionally work onsite with London-area clients.

Design, build, and maintain scalable data pipelines and platform services to support analytics, ML, and product teams. Implement infrastructure-as-code (Terraform) and Kubernetes-based deployments, develop data services/APIs, and ensure data quality, observability, security, and compliance. Partner with cross-functional teams, document systems, and continuously improve performance and reliability.

Build and maintain reliable data pipelines using Python and SQL. Integrate REST APIs, databases, files, cloud storage, and third-party platforms; process and load data into warehouses for analytics and BI. Implement incremental and fault-tolerant loading, logging, monitoring, retries, error handling, and data quality checks. Work with ETL/ELT, Git, and potentially dbt, Spark, orchestration tools, cloud platforms, Docker, dimensional modeling, and BI datasets.

Own and expand third-party billing and usage integrations for a cloud cost platform. Build and maintain ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation, deduplication, and data quality checks. Use AI throughout the engineering workflow, collaborate with product and support teams, and improve integration scalability. The role requires strong SQL and Python, cloud data warehouse experience, REST API integration expertise, and production pipeline ownership.

Designs, develops, and supports secure cloud-based data integration solutions using Microsoft Fabric and Azure. Responsibilities include client discovery, requirements gathering, ETL and ELT development, REST API and SFTP integrations, data pipeline implementation, production monitoring, incident investigation, and reliability improvements. The role collaborates with architects, analysts, project managers, and delivery teams across multiple client engagements.

Build and operate distributed ingestion and processing pipelines, ensure reliability and performance, define data contracts, add observability and testing, improve platform reliability and CI/CD, and participate in design/code reviews and incident retrospectives.

Designs, builds, and maintains scalable data pipelines supporting data science and advanced analytics. Manages reliable Airflow orchestration, delivers data engineering solutions using Databricks, and contributes to GitHub-based collaborative development, code reviews, and engineering standards. Partners with data scientists, supports controlled feature-led delivery, investigates root causes, and develops practical solutions. The role also values adaptability, continuous learning, and exposure to MLOps concepts and tooling.

Designs, builds, and maintains scalable data pipelines supporting data science and advanced analytics. Manages Airflow orchestration and delivers data engineering solutions using Databricks. Collaborates through GitHub, code reviews, and shared engineering standards in feature-led environments. Partners with data scientists, owns delivery outcomes, performs root-cause analysis, and continuously improves pipeline reliability, observability, and maintainability. Exposure to MLOps concepts and tooling is desirable.

Leads electrical design for hyperscale and mission-critical data centers across Europe. Responsibilities include managing HV/MV/LV systems, substations, utilities, consultants, permitting, project delivery, construction support, commissioning, and integrated systems testing. The role provides client-side technical leadership, reviews designs and project documents, coordinates multidisciplinary teams, manages risks and milestones, and ensures compliance with standards and operational requirements.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Build, maintain, and troubleshoot scalable AWS data pipelines using Python, PySpark, Spark, and Airflow. Perform root-cause analysis of pipeline and data-quality issues, apply dimensional modeling and slowly changing dimensions, and deliver governed data assets. Provision infrastructure with Terraform, containerize solutions with Docker, and manage GitLab CI/CD deployments. Work with AWS security, encryption, IAM, monitoring, and analytics services in a regulated government environment.

Leads a Data Products team designing, developing, and evolving foundational data products for energy-market modeling, analytics, and client-facing systems. Owns data product quality, reliability, governance, schemas, versioning, metadata, and data contracts. Provides technical leadership, collaborates with subject matter experts and platform engineers, establishes engineering best practices, and supports agile delivery and continuous improvement.

As a Senior Data Science Engineer, you'll lead data science projects, develop algorithms, analyze datasets, and mentor junior team members to enhance trading operations and strategies.

Lead customer underwriting proof-of-concept projects using SQL, Python, and AWS to transform, validate, and deliver insurance risk data. Match address data to reference identifiers, investigate data-quality issues, document outputs, support underwriting data platforms, and automate manual workflows. Collaborate with technical and non-technical stakeholders, communicate project updates, and improve scalable data processes, quality, and customer experience.

Supports Flight Physics data science projects focused on data analytics, visualization, and machine learning. Develops customizable visualization frameworks using a three-tier web application architecture, analyzes static and dynamic Flight Physics datasets, and contributes to innovation projects that enable strategic transformation. The placement also provides exposure to additional technical topics, software development, and emerging technologies.

Design, build, and own scalable data pipelines from ingestion through analytics and incentive payouts. Lead data platform architecture, including Snowflake, dbt, Iceberg, and S3 workflows. Establish data quality, lineage, observability, performance, cost, and SLA standards. Partner with engineering and analysts to support telematics accuracy and driver incentive reporting. Develop maintainable data models and transformation layers while using AI coding tools to improve development speed and quality.

Build and evolve CluePoints’ enterprise data platform using Microsoft Fabric and Azure. Responsibilities include migrating Azure SQL and ADF workloads, integrating SaaS and web data, developing scalable ingestion and transformation pipelines, maintaining Kimball dimensional models, and establishing CI/CD, data quality, lineage, security, and monitoring. The role partners with analytics leadership and supports trusted Power BI reporting across multiple business functions.

Own and develop the company’s Power BI reporting environment, including semantic models, dashboards, advanced DAX, Power Query transformations, governance, deployment, and performance optimization. Collaborate with Data Engineering and business stakeholders to define KPIs, validate data, improve reporting quality, and deliver scalable BI solutions. Support data architecture, data quality, ad hoc analysis, and continuous improvement across enterprise reporting assets.

Build and maintain cloud software for a Customer Data Platform, delivering complex features from design through production. Lead technical delivery, design scalable solutions, improve code quality, testing, observability, reliability, and security. Collaborate with Product, Design, and adjacent engineering teams, participate in on-call support, contribute to planning and roadmap discussions, and mentor engineers.