Senior Data Engineer

Role Overview

• Design and implement robust, production-grade pipelines using Python, Spark SQL, and Airflow to process high-volume file-based datasets (CSV, Parquet, JSON).
• Lead efforts to canonicalize raw healthcare data (837 claims, EHR, partner data, flat files) into internal models.
• Own the full lifecycle of core pipelines — from file ingestion to validated, queryable datasets — ensuring high reliability and performance.
• Onboard new customers by integrating their raw data into internal pipelines and canonical models; collaborate with SMEs, Account Managers, and Product to ensure successful implementation and troubleshooting.
• Build resilient, idempotent transformation logic with data quality checks, validation layers, and observability.
• Refactor and scale existing pipelines to meet growing data and business needs.
• Tune Spark jobs and optimize distributed processing performance.
• Implement schema enforcement and versioning aligned with internal data standards.
• Collaborate deeply with Data Analysts, Data Scientists, Product Managers, Engineering, Platform, SMEs, and AMs to ensure pipelines meet evolving business needs.
• Monitor pipeline health, participate in on-call rotations, and proactively debug and resolve production data flow issues.
• Contribute to the evolution of our data platform — driving toward mature patterns in observability, testing, and automation.
• Build and enhance streaming pipelines (Kafka, SQS, or similar) where needed to support near-real-time data needs.
• Help develop and champion internal best practices around pipeline development and data modeling.

Requirements

• 6+ years of experience as a Data Engineer (or equivalent), building production-grade pipelines.
• Strong expertise in Python, Spark SQL, and Airflow.
• Experience processing large-scale file-based datasets (CSV, Parquet, JSON, etc) in production environments.
• Experience mapping and standardizing raw external data into canonical models.
• Familiarity with AWS (or any cloud), including file storage and distributed compute concepts.
• Experience onboarding new customers and integrating external customer data with non-standard formats.
• Ability to work across teams, manage priorities, and own complex data workflows with minimal supervision.
• Strong written and verbal communication skills — able to explain technical concepts to non-engineering partners.
• Comfortable designing pipelines from scratch and improving existing pipelines.
• Experience working with large-scale or messy datasets (healthcare, financial, logs, etc).
• Experience building or willingness to learn streaming pipelines using tools such as Kafka or SQS.
• Bonus: Familiarity with healthcare data (837, 835, EHR, UB04, claims normalization).

Tech Stack

• Airflow
• AWS
• Cloud
• Kafka
• Python
• Spark
• SQL

Benefits

• Work from anywhere in the US! Machinify is digital-first.
• Full Medical/Dental/Vision for employees & their families
• Flexible and trusting environment where you’ll feel empowered to do your best work
• Unlimited FTO
• Competitive salary, equity, 401(k) including employer match