описание
We are supporting a search for a Senior Data Engineer within a growing life sciences software and solutions company building data-heavy products for scientific and technical teams. This is a strong opportunity for someone who wants to build data systems close to real life sciences problems, not just generic BI or warehouse work. The platform supports complex scientific workflows where experimental data, model inputs, data quality, structure, lineage, and reliability really matter.
задачи
- Designing and building scalable data pipelines and backend data services
- Developing ingestion, transformation, and data-serving components for scientific data
- Building data models and storage schemas for complex experimental datasets
- Connecting data sources, backend services, computational engines, and modelling workflows
- Building batch and streaming workflows using tools such as Airflow, Flyte, or similar
- Improving data validation, quality checks, lineage, observability, and reliability
- Leading data architecture decisions across pipelines, data models, governance, access, quality, lineage, and compliance
- Supporting customer discussions around data requirements, onboarding, solution design, and technical trade-offs
- Working directly with scientists, engineers, customers, and technical stakeholders
требования
- Strong Python experience across backend and data engineering
- Experience designing production data pipelines and ETL/ELT processes
- Strong SQL and relational database experience, ideally PostgreSQL
- Experience with data modelling and query optimisation for large datasets
- Experience with workflow orchestration tools such as Airflow, Flyte, or similar
- Experience with REST APIs, asynchronous processing, and data integration
- Good understanding of cloud-based data systems, containers, CI/CD, and production operations
- Strong focus on data quality, security, maintainability, lineage, and governance
- Confidence working with customers, scientists, engineers, and non-technical stakeholders
- Будет плюсом: Experience with Azure, AWS, or GCP; Docker, Kubernetes, Terraform, or infrastructure-as-code exposure; Experience with Azure Data Factory, Synapse, Microsoft Fabric, ADLS, Databricks, Spark, Snowflake, or BigQuery; Experience with Kafka, Event Hubs, streaming data, time-series data, or scientific datasets; Familiarity with dbt, Great Expectations, Pandera, OpenLineage, data catalogues, or metadata tooling; Experience in life sciences, biotech, pharma, regulated software, scientific SaaS, or complex technical data environments
условия
- Условий в вакансии нет
