Data Observability Lead
We are looking for an experienced Data Observability Lead to drive the design and implementation of data monitoring, reliability, and governance solutions across our enterprise data ecosystem. This role will combine technical hands-on skills with leadership responsibilities to ensure that data pipelines and platforms remain reliable, transparent, and trustworthy for business-critical decisions.
What You Will Do :
- Lead the design, implementation, and operation of data observability platforms to track data quality, freshness, lineage, and reliability.
- Define and implement cross-pipeline monitoring frameworks to proactively detect anomalies, drift, or issues across data sources.
- Collaborate with data engineering, analytics, and platform teams to integrate observability solutions into existing data pipelines (on-prem and cloud).
- Establish standards for data health metrics, SLAs, and error handling across ingestion, transformation, and consumption layers.
- Manage data lineage tracking, schema evolution monitoring, and compliance reporting.
- Define and evangelize best practices for monitoring, alerting, dashboards, and incident response related to data pipelines.
- Research and apply modern observability tools (Monte Carlo, Databand, Bigeye, OpenLineage, Great Expectations, custom frameworks).
- Build infrastructure-as-code (Terraform / CloudFormation) to deploy observability components consistently across environments.
- Engage in architecture discussions and technology roadmaps to embed observability into the core of our data strategy.
- Mentor junior engineers and lead sprint deliverables focused on pipeline reliability and monitoring.
What Experience You Need :
Bachelors / Masters in Computer Science, Data Engineering, or related field.8+ years of professional experience in data engineering, software development, or reliability engineering, with at least 3+ years dedicated to observability or monitoring.Strong coding skills in Python, Java, or Scala for building observability automation.Deep expertise with data engineering frameworks (Spark, Airflow, dbt, Kafka, Glue, EMR).Hands-on experience with observability / monitoring / logging tools (Datadog, Prometheus, Grafana, Monte Carlo, OpenTelemetry, OpenLineage).Strong understanding of cloud data ecosystems (AWS, GCP, or Azure).Familiarity with DevOps / CI-CD practices including Git, Jenkins / GitHub Actions, Docker, Kubernetes.Knowledge of frontend tools (React / Angular, JavaScript / TypeScript) for custom observability dashboards is a plus.Excellent problem-solving, debugging, and system-level thinking abilities.(ref : hirist.tech)