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.
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.
Ensure business data assets are delivered as trusted, discoverable, and reusable data products/services for broader enterprise consumption, in alignment with strategic data principles.
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.
Experience with DevOps/DataOps practices, including environment management, release management, infrastructure automation, monitoring, and production support.
Strong understanding of FAIR data principles and data product best practices, including discoverability, metadata, lineage, interoperability, access controls, versioning, and consumer-oriented design.
Desired Skills
Familiarity with metadata and catalog tooling such as Collibra to improve lineage, standards adoption, reuse, and observability.
Experience building lightweight application or service layers that complement data pipelines, such as APIs, internal tools, or operational utilities.
Summary
The Full Stack Data Engineer is responsible for the design, development, deployment, and operational support of scalable data products and data applications in a DevOps/DataOps delivery model. This role combines strong data engineering expertise with a solid software engineering background, especially in Python, cloud-native development, and engineering automation.
The role is expected not only to build reliable data solutions, but also to raise engineering maturity across the team by disseminating best practices in software design, testing, CI/CD, observability, reuse, and operational excellence.
Date Posted
27-Jul-2026
Closing Date
06-Aug-2026
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.
