Data Platform Engineer, Versana

• B.S. or B.A in Computer Science or related field
• 5–7 years in data engineering or analytics engineering
• Strong SQL and practical experience with dbt for transformations and testing
• Hands-on experience with data modeling and performance optimization
• Professional coding experience with both Java and Python
• Experience operating data ware/lake-houses and building semantic layers
• Familiarity with CI/CD (DataOps), version control, and environment promotion
• Data quality, observability, and alerting experience with a focus on SLAs and stakeholder trust
• Experience with external client reporting, embedded analytics, and multi-tenant considerations (modeling, partitioning, access controls)
• Demonstrated ability to mentor or coach in software engineering practices
• Effective communication and requirement gathering

Desirable

• Versana is looking for a motivated mid-level Data Platform Engineer to join our team
• This role is primarily focused on data engineering, with a secondary responsibility in reporting and dashboard delivery. Your mission is to transform operational (OLTP) data from multiple source systems into clean, analysis-ready (OLAP) datasets using our lakehouse medallion architecture
• You will work closely with seasoned technology leaders, and colleagues with diverse experience in a dynamic, agile environment
• You’ll mentor team members through code reviews, pairing, documentation, and knowledge-sharing practices. You’ll both share your engineering expertise with colleagues and learn from their deep domain knowledge
• You’ll help standardize data transformations and testing, and drive CI/CD practices, all while improving performance, reliability, and observability
• Design, implement, and operate ELT pipelines to ingest data into the lakehouse
• Apply Medallion architecture and semantic layering to deliver curated datasets
• Build and maintain durable data lakes sourced from operational systems, including sustainable ingestion, schema evolution, storage formats, partitioning, and performance
• Secure data at rest and in transit by enforcing governance policies, including regular access reviews and auditability
• Establish and enforce standards for data quality, observability/alerting, and lineage; uphold daily/hourly SLAs for critical reports and datasets
• Drive CI/CD for data code reviews, documentation, and environment promotion; mentor and unblock teammates
• Create and maintain tabular models and dataflows; optimize dataset refreshes, query performance, and usability
• Partner with Product to translate requirements into robust models, metrics, and dashboards; collaborate with Application Developers on data contracts and change management for upstream systems
• Support operational monitoring and alerting; ensure timely, trusted reporting for leadership and clients
• Triage and fulfill ad-hoc data requests while improving self-service patterns and documentation