We are looking for a passionate Full Stack Data Engineer who will help strengthen our data engineering capability with modern software engineering practices. This individual will build robust, maintainable, and scalable data solutions across the data lifecycle, while also contributing to the team’s wider engineering standards, tooling, and ways of working. 

This is a hands-on engineering role for someone who is equally comfortable developing data pipelines, Python services, and automation for deployment and operations, and who can partner effectively with architects, analysts, product teams, and other engineers to deliver reliable data products. 

Roles & Responsibilities 

• Design, build, and support scalable data pipelines, data products, and data applications that serve business and analytics needs. 

• Apply software engineering best practices to data engineering, including modular design, version control, code review, automated testing, documentation, and maintainable architecture. 

• Develop Python-based solutions for data processing, orchestration, integration, automation, and supporting application components where required. 

• Own and improve DevOps/DataOps practices for data solutions, including CI/CD, environment promotion, release automation, observability, incident response, and production support. 

• Deliver robust, cost-effective, and automated solutions to address recurring business questions and analytical demands. 

• Design and implement data solutions aligned with enterprise standards, architecture roadmaps, and platform best practices, working closely with Data Architects and Solution Architects. 

• Test and quality assure data and analytics solutions to ensure they are fit for release, including code assurance, unit testing, integration testing, data validation, performance tuning, and release management. 

• Support operational excellence through proactive monitoring, root-cause analysis, issue resolution, and continuous improvement of SLAs and service reliability. 

• Promote engineering consistency across the team by disseminating best practices, coaching peers, contributing reusable patterns, and helping improve standards, tooling, and ways of working. 

• Evaluate and adopt new technologies relevant to data engineering, software engineering, and platform automation, including proof-of-value assessments and contribution to business cases. 

• Contribute to estimates, delivery planning, and solution design for new data initiatives and enhancements. 

• Ensure business data assets are delivered as trusted, discoverable, and reusable data products/services for broader enterprise consumption, in alignment with strategic data principles. 

• Collaborate with stakeholders to translate business requirements into reliable technical solutions, define acceptance criteria, and establish appropriate operational and service expectations. 

• Maintain ongoing professional development in modern data, cloud, and engineering practices to help keep AstraZeneca current with a changing technology landscape. 

Mandatory Skills 

• Strong software engineering background, with hands-on experience building production-grade solutions using sound engineering principles such as modular design, testing, code review, and maintainability. 

• Strong Python engineering skills, including building reusable packages, APIs, automation scripts, data processing components, and integration services. 

• Hands-on experience designing and operating solutions in Snowflake, including virtual warehouse configuration, resource monitors, governance, and performance tuning. 

• Expert SQL for analytics and transformation, with strong skills in query optimization, pruning, caching behavior, and result set reuse. 

• Experience building robust pipelines into Snowflake with tools such as dbt, Airflow, dataops.live, Fivetran, AWS Glue, or AWS Lambda, with strong understanding of staging patterns, incremental loads, CDC, retries, error handling, and observability. 

• Practical experience with data modeling, including dimensional and normalized approaches, and strong understanding of schema design, standardization, clustering keys, micro-partitioning, and workload/performance strategies. 

• Experience with dbt modeling layers, materializations, testing, project configuration, documentation standards, and data contracts. 

• Experience implementing automated testing and quality controls for data solutions, including unit, integration, and data validation testing. 

• Strong experience with CI/CD pipelines, Git-based workflows, and deployment automation for data and application components. 

• Experience with DevOps/DataOps practices, including environment management, release management, infrastructure automation, monitoring, and production support. 

• Experience integrating Python-based data engineering solutions with serverless and cloud-native services, such as AWS Lambda and AWS Glue. 

• Demonstrated track record delivering solutions on modern data platforms such as Snowflake or Redshift, and integrating them with downstream analytics or visualization tools. 

• Strong analytical and problem-solving skills, including diagnosing and resolving production issues in complex data environments. 

• Ability to translate business requirements into reliable technical solutions and data products with clear ownership, SLAs, and acceptance criteria. 

• Strong understanding of FAIR data principles and data product best practices, including discoverability, metadata, lineage, interoperability, access controls, versioning, and consumer-oriented design. 

• Effective working independently and within cross-functional, cross-cultural teams, with the ability to communicate technical concepts clearly to non-technical stakeholders. 

• Demonstrable passion for learning and for improving engineering practices across a team. 

Desired Skills 

• Experience using Starburst/Trino for distributed SQL across heterogeneous data sources. 

• Familiarity with Terraform, GitHub Actions, and highly automated platform or pipeline delivery patterns. 

• Experience with infrastructure as code and environment provisioning in cloud-based data platforms. 

• Familiarity with metadata and catalog tooling such as Collibra to improve lineage, standards adoption, reuse, and observability. 

• Experience using PySpark for large-scale data processing and transformation in distributed environments. 

• Experience building lightweight application or service layers that complement data pipelines, such as APIs, internal tools, or operational utilities. 

• Experience mentoring peers and helping establish engineering standards across a team or community of practice. 

Summary