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Data Reliability Engineer - Hybrid
Job in
Hartford, Hartford County, Connecticut, 06132, USA
Listed on 2026-08-29
Listing for:
The Hartford
Full Time, Part Time
position Listed on 2026-08-29
Job specializations:
-
IT/Tech
Data Engineering
Job Description & How to Apply Below
Staff Reliability Engineer - IE07KE
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.
** The Hartford is seeking a
** dedicated
** Data Reliability Engineer (DRE)
** to focus specifically on the integrity, quality, and availability of our data assets and pipelines. This role is a key partner to the SRE team, concentrating on the "data journey" layer. You will apply SRE principles to data pipelines, ensuring our data products are consistently trustworthy and reliable for all downstream consumers.
This role will have a Hybrid work schedule, with the expectation of working in an office location (Hartford, CT; Chicago, IL; Columbus, OH; and Charlotte, NC) 3 days a week (Tuesday through Thursday).
** 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 back filling, 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*
* + Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
+ Bachelors degree and 5+ year's overall experience in an Infrastructure, Data or related technology organization with increasing responsibilities as a hands-on technologist.
+ 3+ year experience in Data Engineering, Data Quality, or a specialized SRE role within an enterprise data environment.
+ Hands-on experience with data warehousing and data lake technologies, including
** Snowflake** , and cloud environments (
** AWS/GCP** ).
+ Hands-on experience with ETL pipelines using SQL Server Integration Services (SSIS) and SQL Server Management Studio (SSMS).
+ Nice to have:
Collaborate with application teams to support and enhance software solutions utilizing the .NET framework (C# or ) interacting with SQL backend architectures. Develop and maintain robust enterprise web applications using ASP.NET (4.5 and 4.6)
+ Hands-on 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…
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