Pay
In line with similar jobs
$140K–$240K against $127K–$190K, the middle half of what similar US postings state.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
Scientific Data Architect - Biologics Manufacturing & Quality Control
Boston, Massachusetts, United States
Actively hiring, first seen by us 5 days ago- Pay
$140,000–240,000/yearAnnual period assumed — pay source
We are not currently providing visa sponsorship for this position The salary range for this position is $140,000 - $240,000. 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.
Read the full posting- Work setting
On-site — work setting source
Work setup in the employer’s posting: On-site
From the employer’s posting- Employment
- Not stated
Before you apply
- Work authorization
- Must already have the right to work
We are not currently providing visa sponsorship for this position
What you’ll work on
Full postingRun onsite discovery with manufacturing, MSAT, QC, IT and Quality teams to define prioritized use cases
Design reusable product, process and QC data models for biologics manufacturing
Model batch and material genealogy, including splits, pools and drug substance to drug product links
Develop Python parsers, transformations, quality checks and applications on Tetra OS
Map identifiers, units and timestamps across sites and external manufacturing partners
From the employer’s posting
lead onsite discovery with manufacturing, MSAT, QC, IT, and Quality teams
design reusable product, process, and QC data models for biologics manufacturing
Model batch and material genealogy, including splits, pools, intermediate lots, and links between drug substance and drug product
Develop Python parsers, data transformations, quality checks, and applications using Tetra OS
Define mappings across sites and external manufacturing partners, reconciling identifiers, terminology, units, timestamps, and process hierarchies while retaining source provenance
What you’ll bring
All qualificationsCore experience
- Strong Python and SQL skills, with experience developing parsers, transformations, APIs, and analytical applications using tabular and JSON data.
- Ability to communicate architecture decisions and scientific findings to laboratory users, manufacturing teams, engineers, and business leaders.
- Demonstrated understanding of upstream and downstream biologics operations, including cell culture, harvest, chromatography, filtration, and drug-substance production, and how process conditions relate to yield and quality.
- Experience defining and implementing manufacturing or QC use cases such as batch investigations, continued process verification (CPV), yield analysis, raw-material variability assessment, stability trending, or technology transfer.
- Experience delivering solutions in GxP environments, including traceability, data integrity, controlled changes, reproducible calculations, and collaboration with Quality and Validation teams.
- Experience integrating scientific and manufacturing data from systems such as LIMS, laboratory instruments, process historians, MES, ERP, ELN, and quality management systems.
Qualification wording
Strong Python and SQL skills, with experience developing parsers, transformations, APIs, and analytical applications using tabular and JSON data.
Ability to communicate architecture decisions and scientific findings to laboratory users, manufacturing teams, engineers, and business leaders.
Demonstrated understanding of upstream and downstream biologics operations, including cell culture, harvest, chromatography, filtration, and drug-substance production, and how process conditions relate to yield and quality.
Experience defining and implementing manufacturing or QC use cases such as batch investigations, continued process verification (CPV), yield analysis, raw-material variability assessment, stability trending, or technology transfer.
Experience delivering solutions in GxP environments, including traceability, data integrity, controlled changes, reproducible calculations, and collaboration with Quality and Validation teams.
Experience integrating scientific and manufacturing data from systems such as LIMS, laboratory instruments, process historians, MES, ERP, ELN, and quality management systems.
Benefits in the posting
Full benefits wording- 100% employer-paid benefits for all eligible employees and immediate family members
- Unlimited paid time off (PTO)
- 401K
- Company paid Life Insurance, LTD/STD
- We are not currently providing visa sponsorship for this position
From the employer’s posting.
About TetraScience
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.
In the employer’s words · Read in context
Posting record
Report an errorActively hiringFirst seen by us 5 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 30, 20265 days ago
- Recorded sightings
- 6
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 5
Early. Similar jobs usually come down between day 6 and day 54.
From 10,845 similar postings that came down. Coming down is not always a hire.
TetraScience is hiring
10 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Data Engineer hiring is picking up
- Takes jobs down at a usual pace
- 10% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- Databricks
- Snowflake
Where the posting names them
- A minimum of 3-5 years direct experience in biologic drug manufacturing and QC is required, including substantial experience with monoclonal antibodies or other recombinant therapeutic proteins. You have designed data models and delivered analytical or digital use cases using manufacturing and QC data for these modalities. - Strong Python and SQL skills, with experience developing parsers, transformations, APIs, and analytical applications using tabular and JSON data. - Ability to communicate architecture decisions and scientific findings to laboratory users, manufacturing teams, engineers, and business leaders.
- Build use cases for investigation evidence assembly, CPV, cross-batch comparisons, process and yield analysis, and technology transfer. Translate scientific questions into data requirements, calculations, prototypes, and production-ready specifications. - Develop Python parsers, data transformations, quality checks, and applications using Tetra OS. Collaborate with engineering teams to deliver reliable integrations and maintainable data products. - Prepare contextualized datasets for statistical, mechanistic, and AI/ML models. Work with scientific users and applied AI engineers to assess data suitability, interpret outputs, and evaluate performance.
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.
- Process historian and time-series data, chromatography data, statistical process monitoring, and multivariate analysis. - Databricks, cloud data platforms, and manufacturing information models such as ICH, ISA-88 and ISA-95. Benefits
Your next step
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About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job description
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.
As a Scientific Data Architect supporting our partnership with a top 25 global biopharma manufacturing and quality organization, you will design the scientific data models and use cases that connect manufacturing processes, laboratory results, and product quality across manufacturing sites and external partners. Your work will support investigations, continued process verification (CPV), process improvement, technology transfer, and the application of AI to biologics manufacturing.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.
Candidates will be asked to review “The Tetra Way” as part of the interview process.
Who You Are
You combine deep experience in biologics manufacturing and quality control with the technical ability to design, prototype, and implement scientific data solutions. You understand how a manufacturing process generates data, how QC laboratories test for product quality, and how to preserve the relationships between those records.
You are comfortable working directly with manufacturing scientists, Manufacturing Sciences and Technology (MSAT), manufacturing IT, QC, Quality Assurance, data engineers, and product teams. You translate their questions into explicit data requirements, reusable models, and applications they can use in daily work.
You take ownership from initial site discovery through implementation and adoption. You write code, examine source data, test assumptions with scientific users, and demonstrate working solutions.
You have the willingness and ability to travel up to 50% of working time between partner manufacturing sites for discovery, implementation, workshops, training, and adoption support.
Requirements
What You Have Done
What you must bring
- A minimum of 3-5 years direct experience in biologic drug manufacturing and QC is required, including substantial experience with monoclonal antibodies or other recombinant therapeutic proteins. You have designed data models and delivered analytical or digital use cases using manufacturing and QC data for these modalities.
- Strong Python and SQL skills, with experience developing parsers, transformations, APIs, and analytical applications using tabular and JSON data.
- Ability to communicate architecture decisions and scientific findings to laboratory users, manufacturing teams, engineers, and business leaders.
Desired: Manufacturing & Quality
- Relevant life sciences industry experience, with substantial work in biologics manufacturing, process development, MSAT, analytical development, or QC.
- Demonstrated understanding of upstream and downstream biologics operations, including cell culture, harvest, chromatography, filtration, and drug-substance production, and how process conditions relate to yield and quality.
- Experience defining and implementing manufacturing or QC use cases such as batch investigations, continued process verification (CPV), yield analysis, raw-material variability assessment, stability trending, or technology transfer.
- Experience delivering solutions in GxP environments, including traceability, data integrity, controlled changes, reproducible calculations, and collaboration with Quality and Validation teams.
Desired: Data & Technology
- A proven record of designing conceptual, logical, and physical data models that connect products, batches, material lots, process steps, equipment, samples, analytical results, and quality events across systems.
- Experience integrating scientific and manufacturing data from systems such as LIMS, laboratory instruments, process historians, MES, ERP, ELN, and quality management systems.
What You Will Do
- Together with the Scientific Business Analyst (SBA), lead onsite discovery with manufacturing, MSAT, QC, IT, and Quality teams. Map scientific workflows, inspect representative source data, identify gaps, and define prioritized use cases with measurable outcomes and acceptance criteria.
- Together with the platform knowledge engineering team (semantic management, data modeling, ontology), design reusable product, process, and QC data models for biologics manufacturing. Establish shared structures and controlled vocabularies while preserving product-, modality-, and site-specific requirements.
- Model batch and material genealogy, including splits, pools, intermediate lots, and links between drug substance and drug product. Connect process measurements and QC results to the correct batch, unit operation, sample, method version, and specification.
- Define mappings across sites and external manufacturing partners, reconciling identifiers, terminology, units, timestamps, and process hierarchies while retaining source provenance.
- Build use cases for investigation evidence assembly, CPV, cross-batch comparisons, process and yield analysis, and technology transfer. Translate scientific questions into data requirements, calculations, prototypes, and production-ready specifications.
- Develop Python parsers, data transformations, quality checks, and applications using Tetra OS. Collaborate with engineering teams to deliver reliable integrations and maintainable data products.
- Prepare contextualized datasets for statistical, mechanistic, and AI/ML models. Work with scientific users and applied AI engineers to assess data suitability, interpret outputs, and evaluate performance.
- Support verification and validation through documented requirements, source-to-target mappings, test evidence, and reproducible analyses, working within partner Quality processes.
- Demonstrate solutions regularly, incorporate user feedback, and train partner teams to maintain and extend delivered models and applications.
- Multi-site manufacturing data harmonization and external CMO/CDMO data integration.
- Process historian and time-series data, chromatography data, statistical process monitoring, and multivariate analysis.
- Databricks, cloud data platforms, and manufacturing information models such as ICH, ISA-88 and ISA-95.
Benefits
- 100% employer-paid benefits for all eligible employees and immediate family members
- Unlimited paid time off (PTO)
- 401K
- Company paid Life Insurance, LTD/STD
- A culture of continuous improvement where you can grow your career and get coaching
We are not currently providing visa sponsorship for this position
The salary range for this position is $140,000 - $240,000. 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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
We are not currently providing visa sponsorship for this position The salary range for this position is $140,000 - $240,000. 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.
- Location & working pattern
Boston, Massachusetts, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
- A culture of continuous improvement where you can grow your career and get coaching We are not currently providing visa sponsorship for this position The salary range for this position is $140,000 - $240,000. 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.