SDE 2

Ovii's Interpretation of the Role

The SDE II will design, build, and operate a self‑serve data platform that powers batch and streaming workloads for InMobi's advertising products. The role emphasizes reliable pipelines, cloud integration, and Kubernetes‑based operations while supporting on‑call incident response.

Role Snapshot

• Build and maintain scalable batch & streaming pipelines
• Integrate Airflow, Iceberg and cloud services
• Apply data modeling, partitioning and query optimization
• Support Kubernetes‑based platform workloads
• Optimize performance, cost and reliability
• Participate in on‑call incident resolution

Must-Have Requirements

• SQL
• data modeling
• partitioning
• query optimization
• Spark (PySpark/Scala)
• Airflow
• Iceberg/Delta/Hudi
• Kubernetes
• GCP/AWS/Azure
• Python
• data platform engineering
• batch and streaming pipeline development

Nice-to-Have Signals

• Flink (exposure)
• Scala (good‑to‑have)
• Kafka streaming experience
• DBT / Great Expectations
• Observability tools (Prometheus, Grafana)
• Metadata/catalog tools (OpenMetadata, DataHub)
• CI/CD, Git basics
• Terraform / Helm basics
• streaming systems (Kafka, Flink)
• CI/CD and IaC practices

Work Setup

• Location: Lucknow, India
• Work mode: ONSITE
• Employment type: Full-Time

Eligibility Gates

• Visa sponsorship: unknown

Not Specified in JD

• Visa sponsorship
• Salary range
• Remote eligibility
• Education requirement
• Certifications
• Relocation
• Notice period
• Travel
• Security clearance
• Coding test
• Portfolio
• GitHub
• Writing sample
• Cover letter

What You'll Likely Work On

• Design and implement scalable data pipelines for batch and streaming using Spark and optionally Flink
• Create and maintain integrations with Airflow, Iceberg/Delta/Hudi and cloud storage
• Apply best‑practice data modeling, partitioning, and query‑optimization techniques
• Run and troubleshoot Kubernetes‑based data platform services
• Tune pipelines for cost efficiency, latency and reliability
• Build monitoring, logging and data‑quality validation layers
• Join the on‑call rotation and resolve production incidents

Good Fit If You Have

• Familiarity with streaming systems such as Kafka
• Exposure to Flink or other stream‑processing engines
• Experience with DBT, Great Expectations or similar data‑testing tools
• Knowledge of observability stacks like Prometheus and Grafana
• Experience with metadata catalogs (OpenMetadata, DataHub)