Data Engineering built for Pharma & Biotech companies
What is Data Engineering?
We turn scattered data into pipelines your business can actually trust and query. For Pharma & Biotech companies, this means navigating FDA 21 CFR Part 11 (US), GxP compliance while shipping fast.
Who needs Data Engineering for Pharma & Biotech?
- Clinical trial data management complexity
- Regulatory validation requirements for software systems
- Integrating lab equipment data into research platforms
- Data scattered across disconnected systems
- Reports that disagree depending on who pulls them
Best Data Engineering company
Clinical and research software must satisfy validation requirements before it can satisfy users.
Data Engineering shouldn't feel like a gamble — Pharma & Biotech teams in work with us because predictability matters.
Get a Data Engineering proposal tailored to your Pharma & Biotech goals
Book a free 30-minute discovery callWhere most Data Engineering projects go wrong for Pharma & Biotech companies
- Clinical trial data management complexity
- Regulatory validation requirements for software systems
- Integrating lab equipment data into research platforms
- Drug supply chain traceability
- Collaboration tools for distributed research teams
A Data Engineering approach built specifically for Pharma & Biotech
Data warehouse architecture (Snowflake, BigQuery)
ETL/ELT pipeline development
Data quality monitoring and alerting
BI dashboard integration
Data governance and access controls
Core Technology Stack
SnowflakeBigQueryAirflowdbtPythonKafkaAWS Glue
Timeline, investment & compliance
Typical timeline
8-16 weeks
Typical investment
$25,000 - $140,000
Data Engineering engagements in this combination typically need to account for:
- FDA 21 CFR Part 11 (US)
- GxP compliance
- EMA regulations (EU)
- HIPAA for clinical data
Proven Results
"We helped a biotech company cut clinical trial data entry time by 38% with a custom eCRF platform."
Read the full case studyStart your Data Engineering project with a team that knows Pharma & Biotech
Get a free project cost estimateFrequently Asked Questions
Most Data Engineering engagements take 8-16 weeks, depending on scope and how clear the requirements are upfront. We'll give you a firm timeline estimate after a short discovery call, not a generic range.
Data Engineering projects with us typically range from $25,000 - $140,000, scaled to the complexity of your requirements. We provide a detailed, itemized estimate before any work begins so there are no surprises mid-project.
For data engineering, we typically work with Snowflake, BigQuery, Airflow, dbt, chosen based on your specific scalability and integration requirements. We're not tied to a single stack and will recommend the right tools for your situation rather than our default.
A typical data engineering engagement includes data warehouse architecture (snowflake, bigquery), along with the other deliverables outlined in our proposal, such as documentation and a post-launch support window. The exact scope is tailored to your project during discovery.
Most clients come to us for data engineering because of data scattered across disconnected systems, among other related challenges. We start every engagement by mapping your specific pain points before writing a single line of code.
We've delivered multiple pharma & biotech projects addressing challenges like clinical trial data management complexity. We helped a biotech company cut clinical trial data entry time by 38% with a custom eCRF platform.
For pharma & biotech clients, we pay close attention to FDA 21 CFR Part 11 (US), GxP compliance, building these requirements into the architecture from day one rather than retrofitting them later. We'll confirm which specific regulations apply to your business during discovery.
The most common challenges we solve for pharma & biotech clients include clinical trial data management complexity and regulatory validation requirements for software systems. We tailor our discovery process to surface the specific version of these problems your business faces.