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About the role
The role involves monitoring data pipelines, file arrivals, and SLAs to ensure data quality and timely delivery. You will triage alerts, resolve common issues using runbooks, and escalate complex technical problems to the Data Engineering team.
What they look for
Requirements
Candidates must have at least 4 years of experience in data engineering, operations, or production support with strong SQL and basic Python skills. Proficiency in data pipeline management and the ability to work rotational or US-overlap shifts is required.
Full description
Senior Data Operations Engineer
Location: Bengaluru, India
About Machinify
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.
About the Role
This role sits within Data Operations, extending that capability to our Bengaluru hub, with coverage into night-time and over-the-weekend on-call.
Data Operations makes sure every client's data arrives on time and with good quality. You don't need to design pipelines from scratch. You do need to understand how they work, read the code and logs well enough to tell what went wrong, and know healthcare data well enough to spot a file that looks wrong. You'll be the first line of defense: monitoring pipelines and file deliveries, catching quality problems before they reach customers, and handling on-call support.
What You Will Own
- Monitor file arrivals, pipeline runs, and SLAs across Airflow/Spark pipelines, SFTP deliveries, and legacy SQL Server jobs.
- Run data quality checks on incoming and processed data: record counts, missing or invalid fields, duplicates, and unexpected changes from prior files.
- Triage alerts (late or missing files, job failures, data quality failures) and resolve common issues using runbooks: reruns, backfills, file reprocessing, and SFTP or permission fixes.
- Escalate to Data Engineering with a clear diagnosis: what failed, evidence from logs and data, likely cause, and client impact.
- Take part in on-call rotations, including shifts that overlap US business hours.
- Write and improve runbooks, and suggest and help build automation for repeat issues (auto-retries, standard alerts, quality checks).
- Answer basic data questions from Operations and client-facing teams using SQL and observability dashboards.
Qualifications
Required
- 4+ years in data engineering, data analysis, data operations, or production support, with the ability to lead an incident and coach others.
- Strong SQL: joins, aggregations, and comparing datasets to find what's off.
- Basic Python: can read pipeline code and write small scripts.
- Basic understanding of data pipelines: ingestion, transformation, scheduling, and dependencies.
- Attention to detail and a quality mindset. Notices when the numbers don't look right and follows up.
- Clear written communication for incident updates and handoffs.
- Willing to work on-call and rotational or US-overlap shifts.
Preferred
- US healthcare data basics: claims (837/835), eligibility (834), member/provider files.
- Hands-on Airflow (logs, retries, backfills) and Spark basics.
- Linux, SFTP, AWS S3.
- SQL Server Agent / SSIS (legacy pipelines).
- Monitoring tools (Grafana, ELK, or similar).
Equal Employment Opportunity at Machinify
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.
