Description

Data Engineer, Enterprise Data, Analytics and Innovation, Digital Innovation

What You'll Do

Are you passionate about building robust data infrastructure and enabling innovation through engineering excellence? As our Data Engineer, your goal is to own and evolve the foundation of our data infrastructure. You will be central in ensuring data reliability, scalability, and accessibility across our lakehouse and transactional systems. This role is ideal for someone who thrives at the intersection of engineering and innovation, ensuring our data platforms are robust today while enabling the products of tomorrow.

A Day in the Life

Lakehouse and Pipelines

• Design, build, and operate reliable ETL and ELT pipelines in Python and SQL
• Manage ingestion into Bronze, standardization and quality in Silver, and curated serving in Gold layers of our Medallion architecture
• Maintain ingestion from transactional MySQL systems into Vaniam Core to keep production data flows seamless
• Implement observability, data quality checks, and lineage tracking to ensure trust in all downstream datasets

Data Modeling and Governance

• Develop schemas, tables, and views optimized for analytics, APIs, and product use cases
• Apply and enforce best practices for security, privacy, compliance, and access control, ensuring data integrity across sensitive healthcare domains
• Maintain clear and consistent documentation for datasets, pipelines, and operating procedures

Integration of New Data Sources

• Lead the integration of third-party datasets, client-provided sources, and new product-generated data into Vaniam Core
• Partner with product and innovation teams to build repeatable processes for onboarding new data streams
• Ensure harmonization, normalization, and governance across varied data types (scientific, engagement, operational)

Analytics and Predictive Tools

• Collaborate with the innovation team to prototype and productionize analytics, predictive features, and decision-support tools
• Support dashboards, APIs, and services that activate insights for internal stakeholders and clients
• Work closely with Data Science and AI colleagues to ensure engineered pipelines meet modeling and deployment requirements

Reliability and Optimization

• Monitor job execution, storage, and cluster performance, ensuring cost efficiency and uptime
• Troubleshoot and resolve data issues, proactively addressing bottlenecks
• Conduct code reviews, enforce standards, and contribute to CI/CD practices for data pipelines

What You Must Have

Education and Experience

• 5+ years of professional experience in data engineering, ETL, or related roles
• Strong proficiency in Python and SQL for data engineering
• Hands-on experience building and maintaining pipelines in a lakehouse or modern data platform
• Practical understanding of Medallion architectures and layered data design

Skills and Competencies

• Familiarity with modern data stack tools, including:
  • Spark or PySpark
  • Workflow orchestration (Airflow, dbt, or similar)
  • Testing and observability frameworks
  • Containers (Docker) and Git-based version control
• Excellent communication skills, problem-solving mindset, and a collaborative approach

What You Might Have, but Isn't Required

• Experience with Databricks and the Microsoft Azure ecosystem
• Expertise with Delta Lake formats, metadata management, and data catalogs
• Familiarity with healthcare, scientific, or engagement data domains
• Experience exposing analytics through APIs or lightweight microservices

The Team You'll Work Closest With

You will collaborate closely with the innovation team to prototype and productionize analytics solutions. Your main contacts will be Data Science and AI colleagues, product and innovation leaders, and internal stakeholders who rely on data-driven insights. You will work remotely with flexibility, growth opportunities, and the ability to influence how data shapes the future of medical communications, helping to turn raw data into client-ready insights that enable measurable healthcare impact.

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