Data Operations

A collection of real-world Data Operations experience — keeping pipelines running, building monitoring & alerting systems, and ensuring reliable data quality at scale in production environments.

Use Cases

Zero-Downtime Pipeline Operations & SLA Management

At an agri-tech platform, I managed production data pipeline infrastructure serving 50+ business processes and downstream BI teams with SLA commitments. The challenge: no centralized monitoring, pipeline failures were often discovered only after business impact had occurred.

📊 Impact

• Critical pipeline SLA maintained above 90%
• MTTR (Mean Time to Recovery) reduced from hours to minutes
• Downstream BI team notified before dashboards showed stale data
• Solid operational foundation for future DataOps practices
• Significant reduction in data incidents reported by business users

🧩 Tech Stack

Apache Airflow, BigQuery, Fivetran, Slack API, Python, Advanced SQL Monitoring Queries

⚡ Problem Statement

• Hundreds of production pipelines (Airflow + Fivetran + BigQuery) with no centralized monitoring
• No alerting when jobs failed silently
• Downstream dashboard impact only discovered after business team reported issues
• No clear incident response process — recovery was slow and undocumented
• Pipeline failures caused data gaps that were hard to detect and recover

🧠 Solution Overview

• Built monitoring dashboard using BigQuery INFORMATION_SCHEMA + Airflow REST API
• Configured Slack alerting for job failure, long-running jobs, and SLA breaches
• Established runbook & tiered incident response process (P0: business-critical, P1: BI downstream, P2: batch)
• Created pipeline priority tiers (critical vs non-critical) for proper SLA assignment
• Documented common root cause patterns + recovery procedures per pipeline type

🏗️ Architecture

1. Monitoring Layer: Airflow REST API + BigQuery INFORMATION_SCHEMA queries for real-time pipeline status
2. Alerting Layer: Slack webhooks for failure, SLA breach, and data freshness threshold alerts
3. Incident Response: tiered P0/P1/P2 priority system with clear escalation paths
4. Runbook: documented common root cause patterns + recovery procedures per pipeline type
5. Reporting: weekly pipeline health report for engineering lead visibility

🔥 Challenges & Solutions

• Silent failures hard to detect without active monitoring — solved with per-table freshness monitoring queries
• False positive alerts causing alert fatigue — tuned thresholds per pipeline characteristics
• Balancing operational fire-fighting with proactive improvements — solved with clear SLA prioritization

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Data Quality Framework & Automated Reconciliation Pipeline

Built a proactive data quality monitoring framework that automatically detects anomalies, stale data, and source-to-warehouse reconciliation failures — transforming a reactive approach into structured, data-driven observability.

📊 Impact

• 70%+ reduction in data quality incidents reported by business users
• Row count reconciliation caught 3 major pipeline bugs before business impact
• Freshness monitoring ensured dashboard data never exceeded SLA thresholds
• DQ coverage reached 100% of critical business tables within 2 sprints
• Weekly data quality scorecard visibility for stakeholder review

🧩 Tech Stack

Python, BigQuery, Apache Airflow, Advanced SQL, Slack API, Google Data Studio, dbt (validation layer)

⚡ Problem Statement

• Data quality issues found by end business users — too late to fix without business impact
• No row count validation between source systems and data warehouse
• No detection of outlier values or sudden metric drops
• Pipeline SLA not tracked — failures only discovered the next day
• No agreed data quality definition standards across teams

🧠 Solution Overview

• Automated DQ checks: row count reconciliation, null rate analysis, min/max boundary checks per table
• Freshness monitoring: per-table last-updated timestamp checks with SLA-based thresholds
• Alerting integration: Slack notifications with actionable metadata (table, row count, delta, threshold)
• Data quality scorecard for weekly review with business stakeholders
• Schema drift detection to handle schema changes that would break existing checks

🏗️ Architecture

1. DQ Check Layer: Python scripts running post-load for per-table validation (null, range, count)
2. Reconciliation Layer: source count vs BigQuery count comparison with tunable threshold tolerance
3. Freshness Monitor: per-table last-updated timestamp checks with SLA-based thresholds
4. Alerting: Slack webhooks with contextual summary (table name, delta, severity, recovery steps)
5. Reporting: weekly data quality scorecard using BigQuery views + Google Data Studio

🔥 Challenges & Solutions

• Defining appropriate tolerance thresholds per table without false positives — solved with historical data profiling
• Handling schema changes that broke existing checks — added schema drift detection as a separate layer
• Prioritizing which tables/pipelines to cover first — tiered by business impact

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