We are seeking a Senior Data Engineer to join a growing life sciences software and solutions company that specializes in developing data-intensive products for scientific and technical teams. This position provides a unique opportunity to work on data systems that address real-world life sciences challenges rather than generic business intelligence or warehouse tasks.

The Role

In this role, you will collaborate with backend engineers, data scientists, modeling specialists, domain experts, infrastructure teams, and customer stakeholders. You will design the data foundation for a modern life sciences software platform, encompassing ingestion, transformation, data models, data services, orchestration, quality checks, governance, and customer-facing data architecture.

Responsibilities

• Design and construct scalable data pipelines and backend data services.
• Develop components for data ingestion, transformation, and serving scientific data.
• Create data models and storage schemas for complex experimental datasets.
• Integrate data sources, backend services, computational engines, and modeling workflows.
• Build batch and streaming workflows using tools like Airflow, Flyte, or similar.
• Enhance data validation, quality checks, lineage, observability, and reliability.
• Lead data architecture decisions related to pipelines, data models, governance, access, quality, lineage, and compliance.
• Support customer discussions regarding data requirements, onboarding, solution design, and technical trade-offs.
• Collaborate directly with scientists, engineers, customers, and technical stakeholders.

Requirements

• Strong experience in Python for backend and data engineering.
• Proven track record in designing production data pipelines and ETL/ELT processes.
• Proficient in SQL and relational databases, preferably PostgreSQL.
• Experience in data modeling and query optimization for large datasets.
• Familiarity with workflow orchestration tools such as Airflow, Flyte, or similar.
• Knowledge of REST APIs, asynchronous processing, and data integration.
• Solid understanding of cloud-based data systems, containers, CI/CD, and production operations.
• Strong emphasis on data quality, security, maintainability, lineage, and governance.
• Comfortable engaging with customers, scientists, engineers, and non-technical stakeholders.

Nice to Have

• Experience with Azure, AWS, or GCP.
• Familiarity with Docker, Kubernetes, Terraform, or infrastructure-as-code.
• Experience with Azure Data Factory, Synapse, Microsoft Fabric, ADLS, Databricks, Spark, Snowflake, or BigQuery.
• Knowledge of Kafka, Event Hubs, streaming data, time-series data, or scientific datasets.
• Familiarity with dbt, Great Expectations, Pandera, OpenLineage, data catalogs, or metadata tooling.
• Background in life sciences, biotech, pharma, regulated software, scientific SaaS, or complex technical data environments.

This position is not a generic data platform role; it is tailored for individuals eager to develop the data layer that supports complex life sciences software, where data quality, structure, and traceability are crucial for the effectiveness of scientific and technical teams.

If you are interested, please apply here, or reach out if you know someone in your network who specializes in scientific data platforms, life sciences data engineering, regulated data systems, or complex scientific data.