IND Staff Engineer, Reliability
The Hartford
India GCC-Puppalaguda VillagePosted Discovered via the company's careers siteMatch locked
Full time
About the role
IND Staff Engineer, Reliability - GCC070
We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.
Key Responsibilities
•
Data Reliability & Quality
Establish and enforce
Data Service Level Objectives (SLOs)
focused on data freshness, completeness, and accuracy across critical data products.
•
Data Observability
Implement advanced data observability tools to monitor the entire
data journey
- from ingestion to consumption - detecting data quality anomalies, schema drifts, and pipeline delays in real-time.
•
Pipeline Resiliency & Automation
Collaborate with Data Engineering to embed reliability patterns into data pipelines built using
Informatica
,
Python/Pyspark
, and running on platforms like
Amazon EMR/Hadoop, Informatica
and cloud native services.
•
Toil Elimination in Data Operations
Automate data validation, data reprocessing, data backfilling, and other manual operational tasks within the data lifecycle to reduce toil and improve operational efficiency.
•
Incident and Problem Management (Data Focus)
Lead the response and resolution for data-related incidents (e.g., corrupt data, delayed reporting), ensuring fast recovery and effective post-incident reviews (blameless post-mortems).
•
Runbook Creation & Automation (Data Focus)
Develop and automate sophisticated, data-aware runbooks for common data pipeline failures, data quality issues, and data recovery scenarios.
Required Skills & Experience
- Experience in an Infrastructure, Data or related technology organization with increasing responsibilities as a hands-on technologist.
- Experience in Data Engineering, Data Quality, or a specialized SRE role within an enterprise data environment.
- Expert level, hands-on experience with data warehousing and data lake technologies, including
Snowflake
, and cloud environments (
AWS/GCP
).
- Expert level experience in pipeline development and support using technologies like
Informatica
,
Python/Pyspark
, and distributed compute (EMR/Hadoop).
- Experience in designing and implementing data quality checks, data validation frameworks, and data governance standards.
- Hands on experience in software or cloud engineering. Familiarity with cloud service providers and their core capabilities (compute, containers, databases, APIs etc.).
- In depth and hands on experience with data observability concepts and tools for monitoring data in motion and at rest (e.g., Monte Carlo, Bigeye, Astro Observe, Datafold, custom solutions).
- A strong understanding of the "data journey" and the impact of data issues on business outcomes.
- Expertise implementing AIOps to monitor, manage and self-heal data pipelines, using machine learning principles for anomaly detection.
- Experience with prompt engineering, implementing AWS or Google AI services, AI enabled automation for data quality, reliability and pipeline performance management.
- Expertise implementing and managing Apache Airflow workflows.
- Expertise defining and implementing of DataOps practices
About Us | Our Culture | What It's Like to Work Here
Not included in the source posting: about the role, benefits.
Skills
artificial-intelligenceawshadooppythonairflowgcpmachine-learningrest-apisnowflakesre
Who can apply
The employer didn't state any visa, work authorization, citizenship or clearance requirements in this posting. Confirm with the employer before applying.
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