Please note I have direct access to the Hiring Director on this position.
Position is 100% Remote. No travel required.
This is an outstanding opportunity for a Sr. Data Architect to be solely responsible for the design and buildout of an enterprise level Data Architecture based on Databricks.
We are the leading Global Biotech Company in our niche and are looking for a Sr. Data Architect. We have been in business for 50+ years, are publicly traded, have 7+K global employees, recognized $6.6 billion in revenues (7% increase over previous year), $153 million in profits (32% increase over previous year), have 750+ product patents, released 6 new products in in the past year and are an active philanthropic institution.
The Sr. Data Architect will lead the ground-up design and technical execution of our enterprise data ecosystem within the organization. Serving as the primary Subject Matter Expert (SME), this role will architect a greenfield Databricks Lakehouse platform, data pipelines, and reporting and analytics frameworks. The Sr. Data Architect will define enterprise data standards, lead a team of 3 Data Engineers, establish data governance, and collaborate across enterprise teams and global sister companies and subsidiaries to build a scalable, secure, and compliant data foundation for advanced analytics and AI/ML.
Responsibilities:
- Greenfield Databricks Lakehouse Design: Solely responsible for the end-to-end architecture and deployment of a modern Databricks Lakehouse, Delta Lake storage layer, and ETL/ELT data pipelines
- Architecture Tooling Selection: Evaluate, select, and implement enterprise data modeling and architecture tools (such as Erwin Data Modeler, Sparx EA, Hackolade, or dbdiagram.io) to establish formal blueprints and schema designs.
- Data Governance: Implement and enforce end-to-end data governance, automated data lineage, quality controls, cataloging (we use Databricks Unity Catalog), Security and Compliance
- Data Aggregation & Integration: Architect ingestion pipelines to aggregate structured and unstructured data from several internal enterprise systems (including SAP, Business Applications, HR, Finance, Operations, etc. platforms) and collaborate with other internal data teams, sister companies and subsidiaries to pull data from distributed Databricks environments.
- Reporting & AI Enablement: Design downstream data models to feed AI/ML and Reporting (we use Power BI) platforms and structure feature-ready data for internal AI/ML teams and digital product innovation.
- Engineering Leadership: Lead, mentor, and guide a dedicated team of 3 Data Engineers in building out data pipelines and execution frameworks based on established architectural standards and governance. If skillset allows, directly assist in pipeline buildout.
Qualifications
- Enterprise Experience: Must 8+ years of experience as an Enterprise Data Architect designing scalable, enterprise-grade data platforms.
- Databricks Expertise: Must have proven experience completing at least 1 greenfield, full-lifecycle Databricks Lakehouse implementation, preferably utilizing Delta Lake storage layer.
- Core Technical Competence: Deep expertise in data modeling (Dimensional/Kimball, Data Vault, etc.), Lakehouse architecture, ETL/ELT pipeline design, preferably with Apache Spark, Python, SQL, and Power BI or equivalents.
- Data Governance Mastery: Demonstrated hands-on experience setting up and managing data governance and access security preferably using Databricks Unity Catalog.
Preferred Skills & Pluses (NOT Mandatory)
- Advanced Analytics & AI/ML: Background supporting machine learning, predictive analytics, or generative AI data ingestion pipelines.
- AI-Assisted Development: Experience developing AI/ML code or using AI coding tools such as Cursor or GitHub Copilot to accelerate code delivery and development workflows is only a plus.
- Regulated Industry Experience: Prior experience operating within regulated environments (FDA, ISO, GxP, GDPR, HIPAA).
- Data Integration Tooling: Exposure to or experience loading data into Informatica or enterprise integration suites.
- DevOps Environment: Familiarity with Azure DevOps for CI/CD pipelines, task tracking, and code repositories.
- Education: Bachelor s or Master s degree in Computer Science, Data Science, Information Systems, or a related field required
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