Data Operations Engineer

Machinify India; Bengaluru; On-site; INR 600,000 - 1,200,000; Full time

Job summary

Machinify India is establishing a Bengaluru hub within Data Operations to monitor and protect data deliveries, ensuring quality before they reach customers. You’ll read logs, understand pipelines, and spot anomalies across Airflow, Spark, and SQL Server jobs, contributing to rapid incident responses.

The role requires 1–3 years in data operations with SQL and basic Python, and a willingness to work on-call shifts overlapping US business hours.

Qualifications

• 1-3 years in data engineering, data analysis, data operations, or production support.
• Strong SQL: joins, aggregations, and comparing datasets.

Responsibilities

• Monitor file arrivals, pipeline runs, and SLAs across Airflow/Spark pipelines, SFTP deliveries, and legacy SQL jobs.
• Run data quality checks on incoming data: record counts, missing fields, duplicates, changes from prior files.
• Triaging alerts and resolving issues using runbooks: reruns, backfills, reprocessing, permissions fixes.
• Escalate to Data Engineering with diagnosis: what failed, evidence, likely cause, client impact.
• Participate in on-call rotations, including overlaps with US hours.
• Write/runbooks and help build automation for repeat issues (auto-retries, alerts).
• Answer basic data questions from Operations and client teams using SQL and dashboards.

Skills

SQL

Python

Data pipelines

On-call readiness

Attention to detail

Clear communication

Tools

Airflow

Spark

Grafana

ELK

Job description

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

• 1-3 years in data engineering, data analysis, data operations, or production support.
• 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.
• 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.