Data Engineer — Client Implementations

Data Engineer — Client Implementations

Location: Bengaluru, India

Machinify builds the AI operating system for healthcare payment intelligence, combining the minds of industry experts with the power of technology. US healthcare wastes billions of dollars a year inside the claims payment workflow, and Machinify has built a platform to fix that, backed by more than 40 years of payment integrity expertise and a product suite spanning the full claims lifecycle from pre-pay through post-pay. Today, 85+ customers rely on us, including 18 of the top 20 US health plans, covering more than 270 million member lives across commercial, Medicare, and Medicaid. We process over 3 petabytes of claims data and return more than $6 billion in savings and recoveries to our customers every year, with 99% customer retention.

We're now opening a new hub in Bengaluru that isn't a satellite office or a support function: it's a genuine extension of one global Machinify, where local leaders run real teams and own real product and outcomes. The problems our teams solve are hard by design, which is exactly why they haven't been solved before, and why the work is worth doing. Joining now means building Machinify's India presence- its people, culture, and way of working- from the ground up, with real influence over what gets built and how.

This role sits within Data Implementations (CPS and RPS lines of business), part of Machinify's Implementations organization.

You'll turn raw external data into powerful, trusted datasets that drive payment, product, and operational decisions. You'll work with PMs, data scientists, subject matter experts, engineers, and customer teams to build, scale, and refine production pipelines so data is accurate, observable, and actionable. You'll be central to onboarding new customers by bringing their raw data into Machinify's canonical models. Your pipelines feed our ML models, dashboards, and core product experiences, including payment integrity, coordination of benefits, subrogation, and audit. The role suits people who like owning end-to-end workflows, shaping data standards, and driving impact in a fast-moving environment.

• Build production-grade pipelines with Python, Spark SQL, and Airflow for large file-based datasets (CSV, Parquet, JSON, fixed-width, flat files).
• Lead the canonicalization of raw healthcare data (837 claims, EHR, eligibility, pharmacy, partner data, flat files) into Machinify's canonical models.
• Own pipelines end to end, from ingestion to validated, queryable datasets, with strong reliability and performance.
• Onboard customers by integrating their data into pipelines and canonical models, working with subject matter experts, Account Managers, and Product on implementation and troubleshooting.
• Handle change requests for live clients: new feeds, layout changes, expansions, and fixes to mapping defects.
• Write resilient, idempotent transformation logic with data quality checks, validation layers, and observability.
• Refactor and scale existing pipelines as data volumes and business needs grow, including tuning Spark jobs and distributed processing.
• Apply schema enforcement and versioning that match internal data standards.
• Monitor pipeline health, take part in on-call rotations, and fix production data flow issues before they spread.
• Use AI agents (Claude Code and internal skills) every day to speed up mapping, code generation, and data file review.

Required

• 3+ years building production data pipelines.
• Strong Python, Spark SQL, and Airflow.
• Production experience with large file-based datasets (CSV, Parquet, JSON, etc.).
• Experience mapping and standardizing raw external data into canonical models.
• Familiarity with AWS or another cloud, including file storage and distributed compute concepts.
• Experience with large or messy datasets (e.g., healthcare, financial, logs).
• Comfort both designing pipelines from scratch and improving existing ones.
• Strong written and spoken English, including explaining technical ideas to non-engineers on US-based teams.

Preferred

• US healthcare data: 837, 835, EHR, UB-04, eligibility (834), pharmacy (NCPDP), claims normalization.
• Streaming experience with Kafka or SQS, or willingness to learn it.
• SQL Server / T-SQL (legacy RPS client databases).

Python, Spark SQL, Airflow, AWS, Kafka/SQS, CSV/Parquet/JSON/flat files, Claude Code.

Machinify is committed to providing equal employment opportunities and fostering an inclusive workplace. Employment decisions are based on business needs, job requirements, and individual qualifications, without discrimination on any status protected under applicable law. If you require a reasonable accommodation to complete any part of the application or recruitment process, please let our recruiting team know.