About Costco Wholesale:

Costco Wholesale is a multi-billion-dollar global retailer with warehouse club operations in eleven countries. They provide a wide selection of quality merchandise, plus the convenience of specialty departments and exclusive member services, all designed to make shopping a pleasurable experience for their members.

About Costco Wholesale India:

At Costco Wholesale India, we foster a collaborative space, working to support Costco Wholesale in developing innovative solutions that improve members’ experiences and make employees’ jobs easier. Our employees play a key role in driving and delivering innovation to establish IT as a core competitive advantage for Costco Wholesale.

Position Summary:

The Data Reliability Engineer – Data Analytics & Data Reliability is responsible for ensuring the availability, reliability, performance, and trustworthiness of enterprise Data & Analytics platforms, data pipelines, data products, and business-facing reporting solutions.

This role provides end-to-end monitoring and production support across the analytics value chain—from source data ingestion and orchestration through BigQuery, Dataflow, Dataproc, Pub/Sub, dbt transformations, GCP, Power BI semantic models and reports, and downstream business consumption. The role applies both Site Reliability Engineering (SRE) and Data Reliability Engineering (DRE) practices so that not only the platforms are available, but the data delivered through them is accurate, complete, timely, consistent, and fit for business use.

The SRE will proactively monitor critical data flows, define reliability targets, detect data and platform anomalies, lead incident response and root cause analysis, automate operational processes, and partner with engineering teams to build reliability and observability into solutions from design through production. The role will participate in 24x7 production support and rotational on-call coverage.

Job Duties/Essential Functions:

- Designs, builds, and maintains end-to-end monitoring and observability across source systems, ingestion, Google Cloud Composer/Airflow, BigQuery, dbt, GCP, Power BI, and downstream reporting and analytics solutions.
- Monitors both system health and data health, including pipeline execution, data freshness, completeness, accuracy, reconciliation, schema changes, volume anomalies, latency, query performance, capacity, report refreshes, and business-facing availability.
- Defines and tracks Service Level Indicators (SLIs), Service Level Objectives (SLOs), availability targets, data freshness thresholds, data quality expectations, and operational KPIs for business-critical data products and analytics services.
- Builds automated controls to detect data quality failures, schema drift, unexpected row-count or volume changes, broken upstream/downstream dependencies, and stale or incomplete data before they materially impact business users.

Data Quality, Trust & Reliability Engineering:

- Partners with Data Engineering, BI Engineering, and Product teams to define reliability expectations for critical data products, including freshness, completeness, accuracy, consistency, and availability.
- Implements automated data validation, reconciliation, dbt tests, anomaly detection,



and production health checks as part of the delivery lifecycle.
- Helps establish data contracts, ownership, and operational expectations for critical interfaces and datasets to reduce unexpected downstream impact from source or schema changes.
- Identifies data downtime and reliability risks, measures recurring failure patterns, and recommends engineering changes that improve confidence in enterprise reporting, self-service analytics, and AI-enabled solutions.
- Ensures critical business metrics and reports have observable dependencies and clearly defined support and escalation paths.

Automation & Operational Excellence:

- Identifies repetitive operational activities and eliminates manual toil through Python, SQL, APIs, scripting, automated health checks, alert enrichment, job recovery, dependency checks, and self-healing workflows where appropriate.
- Develops reusable monitoring and reliability patterns that can be adopted across Data & Analytics engineering teams.
- Builds self-service diagnostic capabilities that enable support and engineering teams to identify failures and business impact quickly.
- Evaluates opportunities for AI-assisted operations, automated anomaly detection, intelligent incident correlation, and automated remediation.
- Continuously improves alert quality to reduce noise and ensure alerts are actionable, prioritized, and tied to meaningful business impact.

Reliability by Design, Performance & Capacity:

- Partners with engineering teams to embed monitoring, logging, alerting, testing, failure handling, recovery procedures, and supportability into solution designs from the beginning.
- Participates in architecture, design, Technical Review Board, release, and production-readiness reviews to ensure reliability and operational requirements are addressed before deployment.
- Monitors and analyzes BigQuery workloads, Composer orchestration, Power BI capacities and refreshes, dbt execution, GVP services, and other platform components for performance, scalability, and capacity risk.

Production Support, Incident & Problem Management:

- Provides end-to-end operational support for production Data & Analytics platforms, pipelines, data products, and reporting solutions.
- Serves as a technical escalation point for high-priority incidents affecting data availability, freshness, quality, performance, or business reporting.
- Leads or actively participates in incident bridge calls, coordinates triage across internal teams and vendors, and drives incidents through restoration, validation, business communication, root cause analysis, and long-term remediation.
- Performs root cause analysis (RCA), identifies corrective and preventative actions, and ensures recurring failures are addressed through permanent engineering solutions rather than repeated manual intervention.

Cross-Functional Collaboration & Communication:

- Works closely with Data Engineering, BI Engineering,



Platform Engineering, Architecture, Quality Engineering, Security, Operations Support, Product Owners, business stakeholders, and technology vendors.
- Communicates production health, incidents, business impact, risks, and remediation plans clearly to technical teams, management, and business stakeholders.
- Shares operational learnings and reliability metrics across teams and influences adoption of consistent Data Reliability and Site Reliability practices.
- Provides technical leadership and mentoring to engineers and support teams and helps strengthen production ownership across Data & Analytics..

Ability to operate vehicles, equipment or machinery:

- Computer, phone, printer, copier, fax

Non-Essential Functions:

- Assists in other areas of the department as necessary.
- Assists in other areas of the company as necessary.

Ability to operate vehicles, equipment or machinery:

- Same as Essential Functions

Experience, Skills, Education & Licenses/Certifications:

Required:

- Experience in the development, maintenance, operation, or support of highly available enterprise Data & Analytics systems.
- Strong experience with production monitoring, observability, incident management, troubleshooting, root cause analysis, and problem management.
- Hands-on experience with modern Data & Analytics technologies such as Dataflow, Dataproc, Pub/Sub, Power BI, BigQuery, DBT, Google Cloud Composer/Airflow, GCP, data pipelines, and cloud data platforms.
- Solid SQL skills and ability to troubleshoot data issues across source, ingestion, transformation, semantic, and reporting layers.
- Experience with data reliability concepts including data freshness, completeness, accuracy, consistency, schema drift, lineage/dependencies, reconciliation, and data quality monitoring.
- Experience defining or supporting SLIs/SLOs, data SLAs, operational KPIs, availability targets, or similar reliability measures.
- Experience developing dashboards, alerts, health checks, and automated monitoring or diagnostic solutions.
- Strong understanding of incident, problem, and change-management processes and ability to lead or support critical production incidents.
- Ability to communicate clearly with business partners, vendors, employees, management, and technical teams and translate technical issues into business impact.
- Ability to work independently, demonstrate strong ownership and accountability, and operate effectively under pressure.
- Scheduling flexibility to meet the needs of a 24x7x365 production environment, including evening, weekend, holiday, and rotational on-call support as needed.

Recommended:

- Experience with Google Cloud Platform, including BigQuery, Cloud Composer, Cloud Monitoring/Logging, and related services.
- Experience with Power BI administration and support, including semantic models, refreshes, gateways, capacities, subscriptions, and report availability monitoring.
- Experience with dbt models, tests, documentation, lineage, and production job monitoring.
- Experience with data observability or monitoring platforms and techniques for anomaly detection, freshness, volume, schema, and quality monitoring.

Other Conditions:

- Management will review the Job Analysis for this position prior to a job offer.

📌 Data Reliability Engineer-30638] (Hyderabad)
🏢 Costco IT
📍 Hyderabad