Senior Data Architect – AWS & Databricks Modernization
Eli Lilly and CompanyMatch Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessMedium due to strong Tier-1 brand and metro Bangalore location, despite niche Databricks specialization reducing applicant density.
High because role requires clinical data quality and regulated-industry domain expertise, reducing cross-industry transferability.
High due to explicit 12+ years requirement and mandatory Databricks, AWS, and migration experience.
Job Description
Structured overview of role & requirementsAbout This Role
Lead architecture and execution of large-scale migrations from legacy warehouses to Databricks lakehouse for Clinical and Non-Clinical data domains.
Build migration pipelines, reusable data products, and define modernization roadmaps ensuring minimal disruption and auditability in a regulated environment.
Drive semantic modeling, data quality frameworks, platform governance, and metrics that support enterprise-wide data-driven decision making.
Minimum Requirements
Minimum 12 years in data architecture/engineering with 3+ years hands-on experience leading Databricks-based lakehouse modernization or migration at scale.
5+ years hands-on experience migrating legacy data warehouses (Oracle, Teradata, SQL Server, Hadoop) onto Databricks in production with multi-domain migration sequencing.
Expertise in Databricks technologies: Delta Lake, Delta Live Tables, Unity Catalog, Databricks Workflows, PySpark, Spark SQL, and CI/CD pipeline deployment.
Bachelor's degree in Computer Science, Information Systems or related discipline; Work Experience Required: Explicitly stated as above.
Ideal Candidate Profile
Experienced in hands-on large-scale lakehouse modernization delivering resilient, governed solutions in regulated industries (pharma/life sciences preferred).
Proficient in designing and shipping semantic models and reusable data products with clear documentation and adoption across multiple data domains.
Comfortable operating both technically (70% coding/engineering) and strategically (30% roadmap/engagement), leveraging AI-assisted tools and driving platform governance in complex enterprise environments.