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
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
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Additional Information
IND - Staff Engineer, Reliability - GCC070
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