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Senior Pharma Data Analyst Jobs (NOW HIRING)

Omni Inclusive

BUSINESS ANALYST

San Mateo, CA

This is a senior-level role requiring deep expertise in life sciences marketing data, HCP analytics, and enterprise data platforms. The ideal candidate understands how the US pharma marketing ...

Curate Partners

Data Governance Architect

Cambridge, MA

$69.75 - $89.50/hr

... pharma data platforms, including Databricks, Collibra, cloud storage, BI, and downstream analytics systems. Define governance patterns for Databricks Lakehouse , including Bronze, Silver, and Gold ...

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Senior Pharma Data Analyst information

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$55K

$99.2K

$135.5K

How much do senior pharma data analyst jobs pay per year?

As of Jun 11, 2026, the average yearly pay for senior pharma data analyst in the United States is $99,231.00, according to ZipRecruiter salary data. Most workers in this role earn between $86,000.00 and $108,500.00 per year, depending on experience, location, and employer.

What does a Senior Pharma Data Analyst do?

A Senior Pharma Data Analyst is responsible for collecting, processing, and analyzing complex data related to pharmaceuticals, such as clinical trial results, market trends, and patient outcomes. They use statistical methods and data visualization tools to interpret information and provide actionable insights to support drug development, regulatory submissions, and business strategies. Additionally, they collaborate with cross-functional teams, ensure data quality, and may mentor junior analysts. Their work helps pharmaceutical companies make informed decisions and improve patient care.

What is the highest paying data analyst job?

Senior Pharma Data Analysts often earn high salaries, especially when working in the pharmaceutical or biotech industries, with top earners making over $100,000 annually. Advanced skills in statistical analysis, data management tools, and industry-specific knowledge can contribute to higher compensation levels.

Is 40 too late for data science?

For a Senior Pharma Data Analyst, age is not a barrier to entering data science, as skills and experience are more important. Many professionals transition into data roles later in their careers by acquiring relevant knowledge in programming, statistics, and tools like SQL or Python. Continuous learning and certifications can help demonstrate expertise regardless of age.

What are the key skills and qualifications needed to thrive as a Senior Pharma Data Analyst, and why are they important?

To thrive as a Senior Pharma Data Analyst, you need advanced analytical skills, a strong background in statistics or life sciences, and experience with pharmaceutical data, typically supported by a relevant degree. Expertise in data analysis tools like SAS, R, Python, and familiarity with clinical trial data standards such as CDISC or FDA regulations is essential. Strong problem-solving, communication, and project management skills help you interpret complex data and collaborate with cross-functional teams. These skills ensure accurate data-driven insights that support regulatory compliance and drive critical decisions in pharmaceutical development.

What are some common challenges Senior Pharma Data Analysts face when working with clinical trial data, and how can they be addressed?

Senior Pharma Data Analysts often encounter challenges such as handling large, complex datasets from multiple sources, ensuring data integrity, and complying with stringent regulatory requirements. Effective collaboration with clinical teams and IT specialists is essential to ensure data accuracy and consistency. Familiarity with industry-standard tools and robust knowledge of data cleaning, validation, and documentation processes can help address these issues. Continuous learning about evolving data standards and regulatory guidelines also supports success in this role.

Is data analyst still relevant in 2026?

Data analysts, including senior pharma data analysts, remain highly relevant in 2026 as organizations continue to rely on data-driven decision-making. Skills in data management, statistical analysis, and tools like SQL and Python are essential, and the role is expected to evolve with advancements in automation and machine learning. Continuous learning and certification in emerging technologies will help maintain relevance in the field.

Is AI replacing data analysts?

AI is transforming the role of senior pharma data analysts by automating routine data processing and analysis tasks, allowing analysts to focus on interpretation and strategic decision-making. While AI tools can enhance efficiency, human expertise remains essential for complex analysis, validation, and contextual understanding in the pharmaceutical industry.
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Senior Pharma Data Analyst jobs near you
Infographic showing various Senior Pharma Data Analyst job openings in the United States as of June 2026, with employment types broken down into 90% Full Time, 8% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $99,231 per year, or $47.7 per hour.
Data Governance at Curate Cambridge, Massachusetts

Data Governance at Curate Cambridge, Massachusetts

disABLEDperson Inc

Cambridge, MA • On-site

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Data Governance Architect

We are seeking a Data Governance Architect to design and implement enterprise data governance capabilities across modern pharma data platforms. The ideal candidate has deep experience in pharmaceutical or life sciences data environments, strong knowledge of Databricks Lakehouse and Medallion Architecture, and hands-on experience with Collibra and Unity catalog for metadata management, business glossary, data cataloging, stewardship workflows, lineage, and governance operating models. This role will define the technical and information architecture required to govern data across R&D, clinical, regulatory, manufacturing, quality, commercial, and enterprise data domains. Must be onsite 3 days a week in Cambridge, MA

Key Responsibilities

  • Design and implement enterprise data governance architecture across pharma data platforms, including Databricks, Collibra, cloud storage, BI, and downstream analytics systems.
  • Define governance patterns for Databricks Lakehouse, including Bronze, Silver, and Gold layers, data ownership, metadata standards, lineage capture, access control, data quality, retention, and certification.
  • Partner with data engineering teams to embed governance controls into Medallion Architecture pipelines, including ingestion standards, transformation rules, validation checkpoints, and curated data product governance.
  • Lead Collibra architecture and configuration for data catalog, business glossary, data domains, assets, policies, stewardship workflows, lineage, and data quality integration.
  • Define metadata models connecting business terms, data products, datasets, reports, pipelines, data owners, data stewards, and regulatory classifications.
  • Architect governance solutions using Databricks Unity Catalog, Collibra, cloud IAM, privacy tools, security controls, and data quality frameworks.
  • Establish architecture standards for sensitive and regulated pharma data, including GxP, HIPAA, GDPR, clinical trial data, patient data, manufacturing quality data, and proprietary R&D data.
  • Create reference architectures, solution blueprints, implementation patterns, data governance standards, and technical design documents.
  • Advise on integration between Collibra and Databricks for metadata harvesting, lineage, catalog synchronization, stewardship workflows, and governance policy enforcement.
  • Support enterprise data product strategy, including data mesh, domain ownership, certified datasets, data marketplaces, and reusable governed data assets.
  • Collaborate with enterprise architecture, data platform, security, privacy, compliance, quality, and business data domain teams.

Required Qualifications

  • 8+ years of experience in data architecture, data governance architecture, information architecture, data management, or enterprise data platforms.
  • Experience working in pharma, biotech, life sciences, healthcare, or another regulated industry.
  • Strong understanding of Databricks Lakehouse Platform, Unity Catalog, Delta Lake, access controls, lineage, workspace patterns, and data platform governance.
  • Hands-on experience with Medallion Architecture, including Bronze, Silver, and Gold data layers.
  • Hands-on experience with Collibra, including operating model design, data catalog, glossary, workflows, stewardship, lineage, policies, and asset model configuration.
  • Strong knowledge of data governance capabilities, including metadata management, lineage, data quality, master/reference data, access management, privacy classification, data lifecycle, and policy management.
  • Experience designing governance frameworks for regulated data, including GxP, 21 CFR Part 11, HIPAA, GDPR, patient data, clinical data, and quality data.
  • Ability to translate business, regulatory, compliance, and privacy requirements into scalable technical architecture.
  • Experience with cloud data platforms, preferably AWS, Azure, or GCP.
  • Excellent documentation, communication, and stakeholder engagement skills.

Preferred Qualifications

  • Experience integrating Collibra with Databricks, Unity Catalog, cloud platforms, BI tools, ETL tools, or data quality tools.
  • Experience with data quality platforms such as Great Expectations, Deequ, Informatica DQ, Collibra DQ, Soda, or Monte Carlo.
  • Experience with data marketplace, data mesh, domain-driven data ownership, and governed data product models.
  • Experience with pharma data domains such as clinical trials, genomics, regulatory submissions, pharmacovigilance, manufacturing, quality, supply chain, medical affairs, or commercial analytics.
  • Knowledge of semantic layers, ontology, master data management, reference data management, and knowledge graphs.
  • Certifications in Databricks, Collibra, CDMP, cloud architecture, or enterprise architecture are a plus.

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