Description

Role Overview
We are looking for a strong DataOps Engineer who can take end-to-end ownership of operational data
integrations, automated ingestion pipelines, and data processing workflows. The primary responsibility of
this role is to maintain, troubleshoot, configure, and support pipelines that move and transform claims and
eligibility data across systems.
This is a hands-on role focused on KTLO operations, pipeline reliability, data quality, automation, and
production support. The candidate should be comfortable working in a distributed engineering
environment and should have a strong sense of ownership around system reliability, data privacy, and
operational excellence.
As a secondary responsibility, the candidate will also support infrastructure deployment and operational
workflows using existing Terraform configurations for AWS resources such as AWS Glue, Athena, and
Lambda, along with CI/CD-based deployment processes.
Key Responsibilities
DataOps / Data Engineering Operations
Own day-to-day operational support for automated data ingestion and transformation pipelines.
Maintain, troubleshoot, and configure pipelines that process claims, eligibility, and related
healthcare data.
Support KTLO activities, including incident triage, data issue investigation, pipeline reruns,
operational fixes, and production monitoring.
Ensure data pipelines are reliable, secure, auditable, and aligned with data privacy and compliance
expectations.
Work closely with distributed engineering, data, and platform teams to resolve pipeline and
infrastructure issues.
Monitor pipeline execution, identify failures or bottlenecks, and drive resolution with clear rootcause analysis.
Support data validation, reconciliation, and quality checks across source and target systems.
Contribute to automation opportunities that improve operational efficiency and reduce manual
intervention.
AWS / Infrastructure / DevOps Support
Deploy, test, monitor, and manage infrastructure using existing Terraform modules and
configurations.
Support AWS-based data processing infrastructure, including:
AWS Glue Catalog
AWS Athena
AWS Lambda
Related IAM, S3, logging, and monitoring components
Execute and troubleshoot CI/CD workflows for data pipeline and infrastructure deployments.
Collaborate with engineering teams to improve deployment reliability, observability, and operational
processes.
Participate in production support activities and help maintain system uptime and reliability.
Required Skills and Experience
Core Data Engineering / DataOps Skills
Demonstrated hands-on experience in data engineering or data operations roles.
Strong experience supporting production data pipelines in a KTLO / operations-heavy environment.
Ability to troubleshoot data pipeline failures, ingestion issues, schema mismatches, data quality
issues, and performance problems.
Experience working with batch or scheduled data processing workflows.
Strong understanding of data movement, transformation, validation, and reconciliation patterns.
Experience working with sensitive or regulated data is preferred.
Programming and Database Skills
Strong proficiency in Python for automation, scripting, data processing, and troubleshooting.
Strong proficiency in PostgreSQL.
Ability to write, debug, and optimize SQL queries.
Comfortable investigating data issues using SQL across operational and analytical datasets.
AWS Skills
Hands-on operational experience with AWS data services, especially:
AWS Glue Catalog
AWS Athena
AWS Lambda
Familiarity with S3-based data lakes or file-based ingestion patterns.
Ability to troubleshoot AWS service issues related to permissions, execution failures, logs, and data
access.
Understanding of IAM, CloudWatch logs, and basic AWS operational practices.
Terraform / DevOps / CI/CD Skills
Hands-on experience deploying infrastructure using Terraform.
Ability to work with existing Terraform configurations and execute infrastructure changes safely.
Experience with Git-based workflows, preferably GitHub.
Experience supporting CI/CD pipelines for application, data, or infrastructure deployments.
Understanding of environment-based deployments, release processes, and rollback practices.
2
Preferred Qualifications
Prior experience in healthcare, insurance, claims, eligibility, or other regulated data environments.
Experience with data privacy, compliance, or handling sensitive customer data.
Experience with operational monitoring, alerting, and incident management.
Familiarity with data cataloging, metadata management, and schema evolution.
Experience improving existing pipelines through automation or operational tooling.
Exposure to orchestration tools or workflow schedulers is a plus.
Experience working with distributed engineering teams

Key Skills

Education

Any Graduate