IND Staff Engineer, Reliability
The Hartford (company site, opens in a new tab)HyderabadOn-siteDirect career pageEst. 30–50 LPA
Posted 16h ago
Reach people at The Hartford
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at The Hartford you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own the reliability and quality of critical data products by establishing and enforcing Data Service Level Objectives (SLOs) targeting freshness, completeness, and accuracy.
- Lead implementation of data observability tools to monitor data pipelines from ingestion to consumption, detecting anomalies, schema drifts, and delays in real-time.
- Drive automation of data operations including data validation, reprocessing, backfilling, and creation of automated runbooks to resolve common data pipeline failures and data quality issues.
Minimum Requirements
- Work Experience Required: Experience in Infrastructure, Data Engineering, Data Quality, or SRE roles within enterprise data environments with increasing technical responsibility.
- Expert level hands-on skills in data warehousing and data lake technologies including Snowflake; cloud environments such as AWS or GCP.
- Expertise in pipeline development/support using Informatica, Python/Pyspark, and distributed compute platforms like Amazon EMR or Hadoop.
- Experience with data observability tools and concepts, and implementing data quality frameworks and governance standards.
Ideal Candidate Profile
- Experienced technologist comfortable in hands-on roles across data engineering, SRE, and cloud software, with strong operational ownership for data reliability.
- Skilled in advanced automation and AI/ML-based AIOps techniques applied to data pipelines, including prompt engineering with cloud AI services.
- Strategic thinker with deep understanding of full data lifecycle, capable of designing systems that reduce manual toil and promptly resolve incidents impacting business outcomes.