IND - Staff Engineer, Reliability

IND - Staff Engineer, Reliability - GCC070
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.

• 8 + year's overall experience in an Infrastructure, Data or related technology organization with increasing responsibilities as a hands-on technologist.

• 2 - 3 + year experience in Data Engineering, Data Quality, or a specialized SRE role within an enterprise data environment.

• H ands-on experience with data warehousing and data lake technologies, including Snowflake , and cloud environments ( AWS/GCP ).

• Hands-on e xperience 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 e xperience 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, Big e ye, 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