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

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