Staff Data Reliability Engineer

The Hartford

Staff Data Reliability Engineer improving data quality, observability, and resilient pipelines for The Hartford insurance company. Automating operations and resolving data incidents across cloud and enterprise platforms.

Posted 8/19/2026full-timeCharlotte • Illinois, North Carolina, Ohio • 🇺🇸 United StatesLead💰 $127,600 - $191,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in Data Engineering and Data Quality, with a strong focus on implementing DataOps practices and advanced data observability tools. Proficient in developing and maintaining data pipelines, ensuring data accuracy, and automating operational tasks within cloud environments.

Highest-signal resume keywords

Data EngineeringData QualityDataOps PracticesAdvanced Data ObservabilityETL Pipeline Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data WarehousingData Lake TechnologiesSQL Server Integration Services (SSIS)SQL Server Management Studio (SSMS)InformaticaPython/PysparkAIOps ImplementationData Validation FrameworksMachine Learning PrinciplesPrompt Engineering

Soft Skills

CollaborationIncident ResponseProblem SolvingEffective Communication

Tools & Technologies

SnowflakeAWSGCPEMRHadoop.NET FrameworkASP.NET

Industry Keywords

Data Service Level ObjectivesData Quality AnomaliesSchema DriftsData Governance StandardsData JourneyCloud Service Providers

Tech Stack

Tools & technologies

ASP.NETAWSCloudETLGoogle Cloud PlatformHadoopInformatica.NETPySparkPythonSQLSSIS

About the role

Key responsibilities & impact

Establish and enforce Data Service Level Objectives focused on data freshness, completeness, and accuracy across critical data products
Implement advanced data observability tools across the data journey from ingestion to consumption
Detect data quality anomalies, schema drifts, and pipeline delays in real time
Collaborate with Data Engineering to embed reliability patterns into data pipelines
Automate data validation, reprocessing, backfilling, and other manual operational tasks
Lead response and resolution for data-related incidents
Ensure fast recovery and conduct effective blameless post-incident reviews
Develop and automate data-aware runbooks for data pipeline failures, data quality issues, and data recovery scenarios
Partner with application teams to support and enhance software solutions utilizing the .NET framework
Develop and maintain robust enterprise web applications using ASP.NET

Requirements

What you’ll need

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
5+ years’ overall experience in an Infrastructure, Data or related technology organization with increasing responsibilities as a hands-on technologist
3+ years’ 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)
Experience in pipeline development and support using Informatica, Python/Pyspark, and distributed compute (EMR/Hadoop)
Experience 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 core capabilities including compute, containers, databases, and APIs
In-depth hands-on experience with data observability concepts and tools
Strong understanding of the data journey and 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 and AWS or Google AI services
Expertise defining and implementing DataOps practices

Benefits

Comp & perks

Short-term or annual bonuses
Long-term incentives
On-the-spot recognition
Total compensation package