Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Specialized senior data-platform role at a Tier-1 firm with broad tech requirements, reducing applicant density.
Core data platform and distributed systems expertise is transferable, but requires specialized big-data tooling experience.
Explicit 7–10 years plus many mandatory data platform, Spark/Flink, Iceberg, Kubernetes, and cloud skills.
Architect and develop a self-serve data platform-as-a-product integrating OSS tools, proprietary services, and cloud/SaaS infrastructure.
Operate and scale distributed systems processing petabytes of data daily on multi-region Kubernetes infrastructure ensuring elasticity and fault tolerance.
Optimize performance and ensure observability and data quality for large scale batch and real-time streaming data workflows with automated SLA enforcement.
7–10 years experience building, optimizing, and operating production data platforms.
Strong expertise in data engineering fundamentals including data modeling, partitioning, query optimization, and data lake architecture (Iceberg, Polaris catalog).
Hands-on experience with distributed compute frameworks Spark (PySpark/Scala), Flink streaming, orchestration with Airflow, data transformation with DBT, and data quality validation frameworks.
Proficiency in Kubernetes platform development, cloud infrastructure (GKE multi-region clusters, GCS), programming (Python, PySpark, Scala), and IaC (Terraform, Helm, GitOps).
Experienced senior engineer comfortable bridging open-source tools and proprietary cloud services into unified production-grade data platform infrastructure.
Demonstrated ability to operate large-scale distributed data systems with multi-region Kubernetes and autoscaling.
Skilled in integrating telemetry and observability frameworks and enforcing data SLAs/SLOs with automated incident management for 24/7 platform uptime.