Full-Stack Data Engineer

We are looking for apassionate Full Stack Data Engineerwho 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 teams wider engineering standards tooling and ways of working.

This is a hands-on engineering role for someone who is equally comfortable developingdata pipelinesPython services andautomation 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 supportscalable data pipelines data products and data applications that serve business and analytics needs.

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

• Develop Python-based solutionsfor data processing orchestration integration automation and supporting application components where.

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

• Deliver robust cost-effective and automated solutionsto address recurring business questions and analytical demands.

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

• Test and quality assuredata 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 excellencethrough proactive monitoring root-cause analysis issue resolution and continuous improvement of SLAs and service reliability.

• Promote engineering consistency across the teambydisseminatingbest practices coaching peers contributing reusable patterns and helping improve standards tooling and ways of working.

• Evaluate and adoptnew technologiesrelevant to data engineering software engineering and platform automation including proof-of-value assessments and contribution to business cases.

• Contribute toestimates delivery planning and solution designfor new data initiatives and enhancements.

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

• Collaborate with stakeholdersto translate business requirements into reliable technical solutions define acceptance criteria andestablishappropriate operationaland service expectations.

• Maintain ongoing professional developmentin 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 inSnowflake including virtual warehouse configuration resource monitors governance and performance tuning.

• Expert SQLfor 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 asdbt AirflowFivetran AWS Glue or AWS Lambda withstrongunderstanding of staging patterns incremental loads CDC retries error handling and observability.

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

• Experience withdbtmodeling layers materializations testing project configuration documentation standards and data contracts.

• Experience implementingautomated testing and quality controlsfor data solutions including unit integration and data validation testing.

• Strong experience withCI/CD pipelinesGit-based workflows and deployment automation for data and application components.

• Experience withDevOps/DataOpspractices including environment management release management infrastructure automation monitoring and production support.

• Experience integrating Python-based data engineering solutions withserverless and cloud-native services such asAWS LambdaandAWS Glue.

• Demonstratedtrack recorddelivering solutions on modern data platforms such asSnowflakeorRedshift andintegrating 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 ofFAIR data principlesand data product best practices including discoverability metadata lineage interoperability access controls versioning and consumer-oriented design.

• Effective working independently and withincross-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 usingStarburst/Trinofor distributed SQL across heterogeneous data sources.

• Familiarity withTerraformGitHub Actions and highly automated platform or pipeline delivery patterns.

• Experience withinfrastructure as codeand environment provisioning in cloud-based data platforms.

• Familiarity with metadata and catalog tooling such asCollibrato improve lineage standards adoption reuse and observability.

• Experience usingPySparkfor large-scale data processing and transformation in distributed environments.

• Experience building lightweightapplicationor service layersthat complement data pipelines such as APIs internal tools or operational utilities.

• Experience mentoring peers and helpingestablishengineering standards across a team or community of practice.

Summary