Principal Software Engineer, Saviynt

• 8+ years of data engineering at production scale across multiple companies
• Demonstrated principal impact: platform standards you defined adopted org-wide, or major cross-team pipeline/schema migrations you led
• Data lake ownership (essential): you have designed and operated a production data lake end-to-end — storage layout, partitioning strategy, tiered retention (hot/warm/cold), table format (Iceberg or Delta Lake), compaction, and access control; not just consumed one
• Deep Spark (PySpark / Scala): executor tuning, shuffle diagnosis, Iceberg table maintenance
• Hands-on Beam / Dataflow: windowing, exactly-once, side inputs, autoscaling
• Schema registry experience: Protobuf / Avro compatibility rules, breaking-change migrations in production
• Orchestration at scale: Flyte, Kubeflow Pipelines, Airflow, or Prefect — operated in production, ideally benchmarked two
• Multi-tenant data architecture: per-tenant isolation as a hard requirement, not a post-hoc concern
• Feature store operations: Feast or Tecton, point-in-time joins, online/offline consistency
• Vector databases: Pgvector or Qdrant in production — index tuning, ANN search, embedding upsert pipelines
• RAG data fundamentals: chunking strategies, embedding model selection, retrieval quality evaluation, and context freshness management
• API transport: gRPC and HTTPS/mTLS for service-to-service communication; comfortable defining proto contracts and managing certificate lifecycle
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience or equivalent military experience

Desirable

• You set the architectural direction for how training data flows, evolves, and is governed across the AI Platform
• You define the standards ML engineers and scientists build on, and ensure every training signal is tenant-isolated, PII-free, and traceable from source to model
• AI Data Lake on GCS: bucket layout, raw → silver → gold tier separation, CMEK encryption, lifecycle rules
• Batch pipelines: Spark on Dataproc for TB-scale feature backfills, Iceberg compaction, and daily S3→GCS incremental sync
• Streaming pipelines: Apache Beam on Dataflow for sub-5-min CDC ingestion with exactly-once semantics and PII assertion gates
• Schema registry: Avro / Protobuf schema versioning, compatibility modes, and migration playbooks for safe schema evolution
• Orchestration: Flyte as primary DAG layer — task authoring standards, domain isolation, retry policies, DataCatalog memoization; evaluate Kubeflow Pipelines where relevant
• Multi-tenancy: strict per-tenant GCS prefix isolation, quota policies, and cross-tenant contamination validation
• Data Anonymizer and Data Labeler microservices: strip PII and attach ML labels before signals leave each customer environment
• Feature store: Feast offline (GCS Parquet) and online (Redis) with point-in-time correctness and < 0.1% consistency SLA
• Vector database: operate Pgvector (Cloud SQL) for POC and Qdrant on GKE for production-scale embedding storage; design index strategies (IVFFlat, HNSW) and manage ANN query latency SLAs
• RAG data pipeline: build embedding generation pipelines that chunk, encode, and upsert document embeddings into the vector store; own the data refresh cadence and staleness SLAs for retrieval context
• Service APIs: expose data platform services (feature serving, embedding upsert, schema validation) over HTTPS with mTLS and gRPC where low-latency streaming is required
• Synthetic data pipelines for dev/staging where real customer data is not permitted
• Data quality gates: Great Expectations / dbt checks as Flyte tasks, blocking on schema and PII-absence failures