Senior Data Engineer — Scientific SaaS / Biopharma
Remote: EU/UK based
We are supporting a search for a Senior Data Engineer to join a growing scientific SaaS business developing technology for biopharmaceutical process development and manufacturing.
The company is building a sophisticated platform combining AI, mechanistic modelling, and bioprocess expertise to create predictive digital twins. As the platform grows, the volume and complexity of scientific data moving through the product is increasing significantly, creating an important opportunity for an experienced Data Engineer to help shape its long-term data architecture.
This is broader than a traditional pipeline-development role. The successful person will have significant ownership across data architecture, backend data services, ingestion, transformation, workflow orchestration, data quality, governance, and platform scalability.
You would be responsible for helping build the data foundation behind the product: ensuring large volumes of scientific and bioprocess data can move reliably between customer data sources, backend services, computational engines, modelling workflows, and downstream applications.
There is also a meaningful customer-facing element. As one of the senior technical specialists within the organisation, you will work directly with biopharmaceutical customers to understand data requirements, explain technical solutions, support onboarding and solution design, and translate scientific requirements into robust technical architecture.
For someone who enjoys combining hands-on engineering with architectural ownership, this offers considerable scope to influence how data is structured, governed, processed, and scaled across the platform.
The role will focus on:
- Designing and developing scalable data pipelines and backend data services
- Building ETL/ELT ingestion, transformation, and data-serving capabilities
- Developing data models and storage architectures for large scientific datasets
- Connecting data sources, backend services, computational engines, and modelling workflows
- Building batch and streaming workflows using Flyte, Airflow, or similar technologies
- Establishing data-quality, lineage, observability, and validation practices
- Defining broader data architecture standards and reference designs
- Establishing governance around quality, cataloguing, access, retention, lineage, and compliance
- Working directly with customers to understand requirements and present solutions
- Supporting pre-sales, onboarding, and solution-design conversations where deep data expertise is required
We are looking for someone with:
- 6+ years of relevant experience
- Strong Python experience across backend and data engineering
- Significant experience building production ETL/ELT pipelines
- Strong PostgreSQL and relational data-modelling experience
- Experience with FastAPI or similar backend frameworks
- Workflow orchestration experience using Flyte, Airflow, or equivalent technologies
- Experience with REST APIs and asynchronous processing patterns
- Strong knowledge of data architecture, quality, security, and maintainability
- Experience designing enterprise-scale data platforms or reference architectures
- Hands-on experience implementing data-governance frameworks
- Strong customer-facing communication skills
Experience with technologies such as Spark, Databricks, Kafka, Snowflake, BigQuery, Delta Lake, dbt, Azure data services, Kubernetes, or Terraform would also be valuable, although the core requirement is strong data-engineering and architectural judgment.
This is a strong opportunity for someone who wants genuine ownership of a data platform rather than responsibility for an isolated set of pipelines. You will have the opportunity to influence the architecture that underpins a technically complex SaaS product and work directly with scientists, engineers, modelling specialists, and major biopharmaceutical customers.
Apply here or if you know someone from your network, please let me know.
