CertifyOS is hiring a Data Analyst to keep the provider data behind its healthcare platform accurate, complete, and fresh. You will monitor data pipelines, validate and reconcile large datasets in Google BigQuery, and build dashboards and quality scorecards that show how healthy the data really is. A big part of the job is data readiness: finding coverage gaps before a client go-live and working with other teams to close them. Your SQL and Python (Pandas) workflows will replace repetitive manual checks, so healthcare organizations can trust the provider networks they depend on.
This role suits analysts who like working with messy, high-volume data and care about whether the numbers can be trusted. At CertifyOS, data quality is the product itself, so your work directly affects whether healthcare organizations can credential, enroll, and monitor providers with confidence.
Why This Role Matters
CertifyOS connects to hundreds of primary data sources to automate provider licensing, enrollment, credentialing, and network monitoring. When a license record is stale or a source stops refreshing, the downstream effect is real: delayed onboarding, compliance risk, or an inaccurate provider directory. As the analyst, you are the person who notices these problems early, traces them to the root cause, and gets them fixed.
Remote Role Based Out Of Bengaluru
A Typical Day In This Role
Your day will likely start with a look at freshness and coverage metrics to check whether overnight pipelines ran cleanly and every data source refreshed on schedule. If something looks off, you investigate in BigQuery, work out whether it is a source delay, an ingestion failure, or a genuine data change, and coordinate a refresh or correction with the relevant team.
The rest of the day usually splits between validation and reconciliation work, preparing datasets for upcoming client launches, and improving the dashboards and scripts the team relies on. Expect regular conversations with engineering, operations, and business teams, because most data issues are only solved with their input.
Core Responsibilities At A Glance
Area | What You Will Own |
|---|---|
Data Monitoring | Track freshness, coverage, and pipeline failures, and escalate issues |
Data Readiness | Identify coverage gaps and confirm quality standards before go-live |
Validation and Reconciliation | Run quality assessments and compare datasets against source records |
Issue Resolution | Investigate anomalies and coordinate refreshes and corrective actions |
Reporting | Build dashboards, quality scorecards, and operational reports |
Automation | Write SQL and Python workflows for recurring checks and analysis |
Systems And Tools You Will Use
The primary stack is the Google Cloud ecosystem. You will write most of your queries in BigQuery , handle files and raw data stored in Google Cloud Storage , and use Python with Pandas for cleaning, preprocessing, and validation. Visualization is done in tools such as Looker Studio , Power BI , or Tableau . Familiarity with Git is a useful extra.
Skills Worth Having Before You Apply
Must Have | Good To Have |
|---|---|
Strong SQL in BigQuery | Healthcare or provider data knowledge |
Python with Pandas | Credentialing or licensing exposure |
Hands-on GCP, mainly BigQuery and GCS | ETL/ELT and data ingestion concepts |
Data cleaning and reconciliation | Git and workflow automation tools |
A dashboarding tool such as Looker Studio, Power BI, or Tableau | Experience with AI-assisted analytics |
Spotting an anomaly is only half the job; explaining its root cause clearly to a non-technical teammate is what makes an analyst valuable.
Problem-Solving Areas
The work is less about running one-off reports and more about investigation. You may compare a provider dataset against its primary source and find mismatched records, or notice that coverage for a particular state has quietly dropped. Another day it could be a pipeline that succeeded technically but delivered incomplete data. Interviewers are likely to look for how you break these problems down, which checks you run first, and how you confirm that a fix actually worked.
What Can Strengthen Your Resume
Freshers can stand out by showing real work instead of listing tools. A project that cleans a messy public dataset in Pandas, loads it into BigQuery, and tracks quality metrics on a dashboard is far more convincing than a certificate alone. If you have worked on duplicate detection, missing value analysis, or data validation rules, describe the problem and the result in plain terms. The Google Cloud Associate Data Practitioner or a similar GCP learning path can also add credibility.
Collaboration And Communication
This role needs someone who can manage their own analytical workflow while keeping several teams informed. You will explain data gaps to operations teams, share findings with engineers, and report quality status to stakeholders. Clear written updates matter as much as clean queries, especially in a remote setup.
Things Worth Learning Early
Provider data basics will help you ramp up quickly: how licenses, NPIs, and credentialing records are structured, and why freshness matters in a regulated industry. On the technical side, practise window functions, joins across large tables, partitioning in BigQuery, and writing reusable Python functions for validation checks.
Keywords For Resume
Data Analyst • SQL • Google BigQuery • Python • Pandas • Google Cloud Platform • GCS • Data Validation • Data Reconciliation • Data Quality • Data Cleaning • Looker Studio • Power BI • Tableau • ETL • Pipeline Monitoring • Dashboards • Automation • Root Cause Analysis • Git
Final Thoughts
This opportunity offers early exposure to data quality work at the infrastructure level, in an industry where accuracy carries real consequences. Candidates who enjoy investigation, automation, and clear communication will find plenty to learn in a high-ownership team.
The above article is written by me, a person interested in technology, automobiles, modern gadgets, movies, music, and clean aesthetics.
