Senior Scientific Data Engineer
At a glance
Senior Data Engineering role at TetraScience. Boston · full-time · $140,000–$200,000 base.
dataroles summary, based on the employer's posting.
What you'll do
- Guide a portfolio of scientific customers from workflow discovery through technical delivery, owning each customer relationship
- Use AI agents to connect Tetra OS with lab systems through two-way pipelines, connectors and Python or TypeScript applications
- Build schemas and parsers for scientific information from instruments, manufacturing and research sources
- Deploy and verify products in cloud, on-premises or hybrid setups, then trace failures across the stack
- Improve product quality and team delivery through design sessions, automated tests, process changes and mentoring
What you bring
- At least eight years creating data products in data engineering or a closely related discipline
- At least eight years applying Python and SQL to data work, system connections and scientific applications
- Deep Python ability and practical familiarity with JavaScript, TypeScript, Node and React
- You have led customer implementation work across teams, controlling requirements, schedules and delivery checkpoints
- You have worked with non-clinical data and laboratory scientists
Who this fits
This is a senior, hands-on role for someone suited to customer-facing technical delivery, cross-functional leadership and mentoring. The position is remote and based in Boston.
From the employer
About TetraScience
TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world’s leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience’s growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft.
In connection with your candidacy, you will be asked to carefully review “The Tetra Way,” authored by our CEO, Patrick Grady; it is impossible to overstate the importance of this document, and you should take it literally as you decide whether our mission, culture, and expectations are right for you.
What You Will Do
You will join TetraScience's forward deployed teams as a senior, hands-on engineer. In this role, you will work directly with biotech, pharmaceutical, and scientific companies, bringing advanced data and AI technologies into the labs that research, develop, and manufacture medicines. You will work alongside customer scientific, engineering, and IT teams to design, build, and ship integrations and data products on Tetra OS that serve scientific teams across the globe. You will also mentor other scientific data engineers, lead internal design sessions, and partner with Product Managers and Solution Architects to turn customer, product, and engineering objectives into commercialized solutions.
- Act as a forward deployed engineer for a portfolio of biotech, pharmaceutical, and scientific customers. Own the technical relationship, understand their scientific workflows, and deliver solutions that move their programs forward.
- Drive AI agents to design and build bidirectional integrations between Tetra OS and customer systems such as ELN, LIMS, MES, and process historians, including serverless push and pull pipelines, stateful connectors, and full stack data applications in Python and TypeScript.
- Drive AI agents to design and build comprehensive data schemas and parsers for scientific data from R&D lab instruments, manufacturing, CRO, CDMO, ELN, and LIMS sources, across formats such as .xlsx, .pdf, .txt, .raw, .fid, and many other vendor binaries.
- Deploy and test solutions in customer environments, including cloud, on-premises, and hybrid networks, and troubleshoot across the full stack when something breaks.
- Work with product managers, solution architects, business analysts, data architects, and ML engineers to understand business requirements, recommend outcomes, and build best-in-class data products based on consensus.
- Create and enforce designs with quality backed by comprehensive test automation to prove expected behaviors.
- Lead process and technology improvements supporting product quality, developer experience, and platform capabilities.
- Rally the team to finish Agile sprint commitments. Actively surface team inefficiencies and resolve them.
- Drive results. Bring pragmatic urgency to resolving blockers and unclear requirements, and make things happen.
- Provide mentorship to junior scientific data engineers and show leadership on every front.
Requirements
- 8+ years building data products as a Data Engineer or in a similar field.
- 8+ years working in Python and SQL with a focus on data, integrations, and scientific applications.
- Expertise in Python and working knowledge of JavaScript, TypeScript, Node, and React.
- History leading customer-facing implementation projects across cross-functional teams, managing requirements, timelines, and delivery milestones.
- Experience working with non-clinical data and lab scientists.
- Familiarity with the biotech and pharmaceutical industry and regulated environments such as GxP.
- Familiarity with networking, CI/CD, and full stack development.
- Familiarity with cloud engineering on AWS, including core services such as S3, Lambda, ECS, SQS, SNS, EC2, and CloudWatch.
- Excellent communication skills, attention to detail, and the confidence to take control of project delivery.
- Ability to quickly understand a highly technical product and communicate effectively with customers, product management, engineering leadership
Benefits
- Competitive compensation
- Stock options in a VC-backed Series C company
- 100% employer-paid benefits for all eligible employees and immediate family members
- 401(k)
- Unlimited paid time off (PTO)
- Flexible working arrangements
- Company-paid Life Insurance, LTD/STD
The salary range for this position is $140,000 - $200,000 USD Annually. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate’s specific experience, localized market data, and internal team equity.
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