Staff Data Reliability Engineer - Hybrid

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

• Bachelor's degree and 5+ years of overall experience in an Infrastructure, Data, or related technology organization with increasing responsibilities as a hands-on technologist.
• 3+ years of 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 VB.NET) 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 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 defining and implementing of DataOps practices.

Compensation

The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is: $127,600 - $191,400.

Equal Opportunity Employer

The Hartford is an equal opportunity employer committed to diversity and inclusion.