Key Responsibilities

Contribute to the design, development, and scaling of core data infrastructure using GCP, Spark, Databricks, and Fivetran
Develop robust and maintainable ETL/ELT workflows that support diverse structured and unstructured data needs
Implement and manage Change Data Capture (CDC) pipelines to enable near real-time data replication and synchronization
Define and enforce data governance and compliance standards, including access control, auditability, lineage, and metadata management
Build and manage streaming and batch data pipelines to serve high-impact use cases across analytics, product, compliance, and experimentation
Act as a strategic partner to cross-functional teams (product, analytics, engineering, clinical) to ensure data is accessible, trustworthy, and impactful
Drive the long-term architectural vision of the data platform to support current and future business and product needs

Requirements

5+ years of experience in software engineering, with a focus on scalable data architectures
Strong expertise in GCP (IAM, GCS, Pub/Sub, etc.) and hands-on experience with Spark and Databricks
Hands-on experience with CDC technologies like Fivetran, or equivalent
Proficiency in ETL/ELT tools and frameworks (dbt, Apache Airflow, Dataform, etc.)
Deep understanding of data governance principles, including compliance and security best practices
Demonstrated success in collaborating across functions to deliver data solutions for analytics, experimentation, or compliance
Balance of IC execution and leadership skills; comfortable rolling up sleeves or mentoring others
Familiarity with streaming data architecture, real-time ingestion, and delivery frameworks
Proficient in SQL and Python for data processing and automation
Strong problem-solving skills with the ability to work in a fast-paced environment
Excellent communication and technical storytelling skills

Experience with Terraform or Infrastructure-as-Code (IaC) for data infrastructure automation
Background in HIPAA or other regulated environments with sensitivity to data privacy and compliance
Familiarity with the dbt Semantic Layer and modern data modeling best practices
Exposure to data observability platforms and practices
Familiarity with machine learning data pipelines
Exposure to multi-cloud or hybrid-cloud environments
Experience building scalable solutions in a 0-1 environment