• Own the design, implementation, and operation of shared Resource Plane services used across the organization. • Operate and evolve Kafka, RabbitMQ, Redis, and Elasticsearch platforms in production. • Apply SRE principles to ensure availability, scalability, reliability, and safe upgrades of stateful systems. • Build and maintain Infrastructure-as-Code and GitOps-based automation for provisioning and lifecycle management. • Define and enforce supported usage patterns, guardrails, and golden paths for platform consumers. • Integrate Resource Plane services into the Internal Developer Platform for self-service consumption. • Participate in on-call rotation (initially may include 24x7 weekly rotation; target state is business-hours primary). • Lead incident response, root cause analysis, and reliability improvements for owned services. • Collaborate closely with the Kubernetes Platform Team, IDP, and other teams as required. • Act as a senior technical voice in architecture discussions and platform roadmap planning.

• Set and drive the technical vision for the team and partner with peer staff/principal engineers to shape direction across adjacent teams (data platform, reporting, internal and external frontends & apps) • Sit at the table with product and business stakeholders (KBRA Analytics, Bank Product) to translate strategy into a multi-quarter technical roadmap, make build/buy/sunset calls, and own the architectural decisions that come out of them. • Own the architecture of a Snowflake lakehouse ingesting 30+ regulatory and market data sources (FFIEC, FDIC, NCUA, FactSet, Xignite, SEC EDGAR, FRED, etc.) via S3 external tables, Dask-based transforms, and SQS / Mongo change-stream event pipelines. • Design and evolve reusable Terraform modules spanning AWS, Azure, Snowflake, MongoDB Atlas, GitLab, Datadog, etc. • Drive CI/CD, IaC, and acceptance-test (BDD/behave) standards across the team. • Mentor engineers and act as code-owner across IaC, data-lake, and application repos.

Title: Principal Software Engineer, AI Platform Engineering Location: Type: Full-Time Workplace: hybrid Category: Platform Upgrade Job Description: ABOUT SAVIYNT Saviynt is a leader in identity security, delivering an AI-powered platform that governs and secures access to applications, data, and business processes for global enterprises and government institutions. Built for the AI era, Saviynt helps organizations move faster &mdash; securely and compliantly. ABOUT THE ROLE 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. WHAT YOU'LL OWN - AI Data Lake on GCS: bucket layout, raw &rarr; silver &rarr; gold tier separation, CMEK encryption, lifecycle rules - Batch pipelines: Spark on Dataproc for TB-scale feature backfills, Iceberg compaction, and daily S3&rarr;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 &mdash; 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 YOU'LL THRIVE HERE IF YOU HAVE - 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 &mdash; 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 &mdash; 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 &mdash; 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 NICE TO HAVE - Differential privacy or k-anonymity for ML training datasets - Open source contributions: Feast, Great Expectations, Apache Beam, or dbt - Familiarity with IAM / access governance data: entitlements, provisioning events, access graphs - Iceberg or Delta Lake at petabyte scale WHY JOIN SAVIYNT - Work on a large-scale, Kubernetes-based SaaS platform - Solve challenging cloud and reliability problems at scale - Collaborate with strong engineers in a reliability-focused culture - Competitive compensation, benefits, and growth opportunities SECURITY & COMPLIANCE This role requires adherence to Saviynt's information security and privacy policies, including annual security training. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

• Provision and manage cloud infrastructure reproducibly using infrastructure as code, likely via Terraform. • Design and maintain CI/CD workflows, including Git-based workflows and GitHub Actions. • Implement and manage observability, monitoring, and alerting using tools like Prometheus, Grafana, or Datadog. • Shape vulnerability scanning, dependency management, and system hardening. • Administer Linux systems, including shell scripting and troubleshooting.