Senior Member of Technical Staff

Senior Software Engineer (IC3) – Lakehouse / Batch Data Platform (Oracle Health Data Intelligence, HDI)

Location: Bangalore, India

About the Role

Oracle Health Data Intelligence (HDI) is hiring a Senior Software Engineer (IC3) to help build and evolve our next-generation Data Platform powering intelligent AI agents at scale. This role is focused on lakehouse and batch processing —designing reliable, scalable ETL/ELT pipelines and foundational data platform services on OCI to ingest, transform, curate, and serve high-quality healthcare data for analytics and AI workloads.

You’ll work closely with platform engineers, data engineers, and applied AI teams to deliver durable, governed datasets and platform capabilities that enable downstream product experiences.

Key Responsibilities

• Build and operate batch-first ETL/ELT pipelines that ingest and transform data into curated lakehouse layers (e.g., raw refined curated).
• Design scalable data processing jobs using distributed compute frameworks (e.g., Spark/Beam) with strong attention to correctness, performance, and cost.
• Contribute to the architecture and evolution of our data lakehouse , including data layout/partitioning, compaction strategies, schema evolution, and backfills/reprocessing.
• Develop and maintain platform components for metadata management , dataset publishing, and pipeline orchestration.
• Implement data quality validation , lineage/metadata capture, and operational best practices (SLAs/SLOs, alerting, runbooks, auditing).
• Optimize pipelines for reliability and efficiency in a distributed environment (idempotency, retries, incremental loads, late-arriving data handling).
• Participate in code reviews, design discussions, and technical planning; collaborate across teams to deliver end-to-end solutions on OCI.

Required Qualifications

• 4-7 years of relevant industry experience in software engineering and/or data engineering.
• Strong programming skills in Java, Python with solid software engineering fundamentals (OO/design, testing, debugging, performance).
• Hands-on experience building large-scale batch pipelines using Apache Spark (preferred) and/or Apache Beam (or equivalent).
• Experience with lakehouse/data platform concepts: partitioning , schema management , incremental processing , file formats (Parquet/ORC), and dataset versioning.
• Exposure to cloud data services (OCI preferred) such as Object Storage , compute, networking/IAM, and managed data/processing services (e.g., Oracle BDS or equivalents on AWS/GCP/Azure).
• Strong understanding of data modeling and governance fundamentals (access controls, auditing, retention, PII handling concepts).
• Practical experience with pipeline observability : metrics, logs, alerts, job monitoring, and troubleshooting production workflows.

Preferred Qualifications (Bonus)

• Experience with feature store , metadata catalogs , or data discovery/governance tooling.
• Familiarity with semantic indexing / vector search (e.g., Oracle Database 23ai vector capabilities) and/or building retrieval datasets for AI workloads.
• Experience with Docker and Kubernetes ; CI/CD for data/compute workloads.
• Healthcare domain exposure and comfort operating in regulated-data environments.

Why Join Oracle HDI

• Work on foundational data infrastructure that directly enables AI-driven healthcare intelligence.
• Build at scale on OCI with high-impact ownership and strong cross-team collaboration.
• Solve challenging problems in data reliability, governance, and performance for real-world enterprise workloads.

Key Responsibilities

• Build and operate batch-first ETL/ELT pipelines that ingest and transform data into curated lakehouse layers (e.g., raw refined curated).
• Design scalable data processing jobs using distributed compute frameworks (e.g., Spark/Beam) with strong attention to correctness, performance, and cost.
• Contribute to the architecture and evolution of our data lakehouse , including data layout/partitioning, compaction strategies, schema evolution, and backfills/reprocessing.
• Develop and maintain platform components for metadata management , dataset publishing, and pipeline orchestration.
• Implement data quality validation , lineage/metadata capture, and operational best practices (SLAs/SLOs, alerting, runbooks, auditing).
• Optimize pipelines for reliability and efficiency in a distributed environment (idempotency, retries, incremental loads, late-arriving data handling).
• Participate in code reviews, design discussions, and technical planning; collaborate across teams to deliver end-to-end solutions on OCI.