CodaMetrix is revolutionizing Revenue Cycle Management with its AI-powered autonomous coding solution, a multi-specialty AI-platform that translates clinical information into accurate sets of medical codes. CodaMetrix’s autonomous coding drives efficiency under fee-for-service and value-based care models and supports improved patient care. We are passionate about getting physicians and healthcare providers away from the keyboard and back to clinical care.

Overview

The Senior Data Engineer is a member of the Data Platform team, reporting to the Director of Machine Learning Engineering and Data. The Data Platform team is responsible for executing the data strategy for the organization, ensuring high-quality external data is ingested into the Lakehouse and realized in powerful insights for internal and external customers, while ensuring ML/AI, BI and customer success teams have the data they need to develop and train their models, build insightful dashboards and design semantic layer.

Responsibilities

Technical strategy and roadmap - Contribute to the vision, architecture, and roadmap for the CodaMetrix data platform, ensuring scalability, reliability, regulatory alignment, and operational excellence. Participate in key technology decisions, disaster recovery design, architecture reviews, and data engineering standards across teams.

Platform Engineering at Scale - Design, build, and maintain scalable streaming and batch data platforms Databricks using PySpark, Unity Catalog, Delta Lake, and Structured Streaming. Own Terraform-based infrastructure across environments, including jobs, catalogs, schemas, permissions, compute policies, volumes, and external locations. Build Jenkins CI/CD workflows for automated testing, tagging, and deployments, while optimizing training pipelines, materialized views, retention policies, and production performance.

Data Governance & Security - Implement and evolve least-privilege access controls across Databricks using Unity Catalog grants, YAML-driven group policies, and schema-level restrictions. Ensure HIPAA and SOC 2 compliance through PHI masking, audit logging, environment-level data segmentation, user provisioning, compute policies, and cost attribution.

Cost Optimization & Operational Excellence - Drive platform cost reduction through compute policy tuning, serverless optimization, reserved pools, materialized view improvements, and remediation of underutilized resources. Monitor Databricks/AWS spend (using tools like CloudZero and AWS Cost Explorer), own cost attribution and budgeting, resolve production incidents, and maintain runbooks to ensure platform SLAs for uptime and performance.

Cross-Functional Enablement & Mentorship - Enable ML, BI, Analytics, DevOps, and customer success and implementation teams with training data pipelines, model-ready datasets, feature store architecture, optimized views, dashboards, Tableau refreshes, infrastructure changes, and tenant onboarding. Mentor engineers through code and design reviews, establish quality standards, and serve as a subject matter expert for data platform engineering across the organization.

Requirements

Must-Haves

A degree in Computer Science or a related field (Bachelor's, Master's, or Ph.D.), or an equivalent combination of education and demonstrable professional experience

3-5+ years of data engineering experience with progressive responsibility

3-5+ years hands-on experience with the Databricks platform (Unity Catalog, Delta Lake, Structured Streaming, Spark SQL, cluster management, platform administration)

Expert proficiency in PySpark and Python; working knowledge of Scala

Expert experience with Terraform for infrastructure-as-code (state management, modular structures, multi-environment deployments, YAML-driven configuration)

Expert experience with Apache Kafka / AWS MSK (streaming ingestion, SASL/IAM auth, topic management, cluster migrations)

Deep understanding of medallion/lakehouse architecture patterns (bronze/silver/gold, SCD2, materialized views, slowly changing dimensions)