TECH SVCS - Data Solutions Engineer

Job-ID29416035Reference26-27824TECH SVCS - Data Solutions Engineer Must Have List:

• 6–10+ years in data engineering
• Strong Databricks experience
• Strong data product and data modeling skills. Proven experience defining and delivering data products with business stakeholders, not just executing handed-down specifications.
• Demonstrated hands-on Databricks experience: PySpark, Spark SQL, Delta Lake, and experience with medallion architecture (Bronze/Raw, Silver/Refined, Gold/Published) including what logic belongs in which layer and why.
• Advanced SQL, Python, and a Git-based development workflow.

Job Title: Data Manager/Data Solutions Engineer Work Location: 50% onsite at Princeton Pike Hours: Mon-Fri 8am-5pm

ROLE SUMMARY

The Company’s Commercialization Business Insights & Technology (CBI&T) Dat and AI (D&A) New Build team is responsible for executing on prioritized work leveraging Databricks as its data foundation platform. Seeking US Data Lead to manage delivery of these data assets and data products end to end. This position is the technical point of contact for this work, translating business requirements from our Commercialization AI Strategy & Analytics (CASA) business partners into designed, built, tested, and released data products on Databricks. Work with US data team members, other commercialization BI&T stakeholders, company offshore engineers and systems integrator resources.

KEY RESPONSIBILITIES

Data delivery

• Lead design, build, UAT, releases
• Partner with CASA data owners, domain leads, and business SMEs to translate data product scope: critical data elements, business rules, metric definitions, grain, aggregation logic, and acceptance criteria into technical design and execution.
• Produce core design artifacts: source-to-target mappings, analytical requirement documents, Bronze, Silver Gold-layer logical data models.

Technical build and data modeling

• Hands on understanding of of Databricks tools, (PySpark, Spark SQL, Delta Lake, etc.)
• Apply platform standards for Unity Catalog cataloging, table design and clustering, performance tuning, data quality controls, and CI/CD through Git.
• Data Product data modeling

Offshore/Vendor Engagement

• Day-to-day work of an offshore engineering pod in Hyderabad, India — backlog grooming, task assignment, design review, and code review.
• Provide technical direction to systems integrator and consulting partners working on the same domains, and hold their deliverables to company standards.
• Review vendor work products against statement-of-work scope and flag gaps early.

REQUIRED QUALIFICATIONS

• 6–10+ years in data engineering
• Databricks experience
• Strong data product and data modeling skills. Proven experience defining and delivering data products with business stakeholders, not just executing handed-down specifications.
• Demonstrated hands-on Databricks experience: PySpark, Spark SQL, Delta Lake, and experience with medallion architecture (Bronze/Raw, Silver/Refined, Gold/Published) including what logic belongs in which layer and why.
• Experience with Unity Catalog: catalogs and schemas, access control, and data classification.
• Experience leading distributed and offshore teams, with demonstrated ability to get quality output from an India-based delivery pod.
• Advanced SQL, Python, and a Git-based development workflow.
• Excellent written and verbal communication; able work independently with business stakeholders
• Self-directed. This team needs people who can take an ambiguous business ask and drive it to released data products

PREFERRED QUALIFICATIONS

• Pharmaceutical or life sciences commercial data experience, and familiarity with industry data sources such as IQVIA, Symphony Health, Komodo, Flatiron, specialty pharmacy and distributor data, Veeva CRM, and marketing or digital engagement platforms.
• Understanding of commercial analytics use cases: field force performance, customer, patient journey and line of therapy
• Familiarity with data governance concepts: data ownership, stewardship, critical data elements, and data quality frameworks.
• Experience in a regulated environment with audit, privacy, and access-control requirements

EEO:

“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”