Palantir Foundry Engineer - Nashville TN (Onsite Day One)

Role Summary Hands-on Foundry specialist who can design ontology-first data products, engineer high-reliability pipelines, and operationalize them into secure, observable, and reusable building blocks used by multiple applications (Workshop/Slate, AIP/Actions). You’ll own the full lifecycle: from raw sources to governed, versioned, materialized datasets wired into operational apps and AIP agents. Core Responsibilities • Ontology & Data Product Design: Model Object Types, relationships, and semantics; enforce schema evolution strategies; define authoritative datasets with lineage and provenance. • Pipelines & Materializations: Build Code Workbook transforms (SQL, PySpark/Scala), orchestrate multi-stage DAGs, tune cluster/runtime parameters, and implement incremental + snapshot patterns with backfills and recovery. • Operationalization: Configure schedules, SLAs/SLOs, alerts/health checks, and data quality tests (constraints, anomaly/volume checks); implement idempotency, checkpointing, and graceful retries. • Governance & Security: Apply RBAC, object-level permissions, policy tags/PII handling, and least-privilege patterns; integrate with enterprise identity; document data contracts. • Performance Engineering: Optimize joins/partitions, caching/materialization strategies, file layout (e.g., Parquet/Delta), and shuffle minimization; instrument with runtime metrics and cost controls. • Dev Productivity & SDLC: Use Git-backed code repos, branching/versioning, code reviews, unit/integration tests for transforms; templatize patterns for reuse across domains. • Applications & Interfaces: Expose ontology-backed data to Workshop/Slate apps wire Actions and AIP agents to governed datasets; publish clean APIs/feeds for downstream systems. • Reliability & Incident Response: Own on-call for data products, run RCAs, create runbooks, and drive preventive engineering. • Documentation & Enablement: Produce playbooks, data product specs, and runbooks; mentor engineers and analysts on Foundry best practices. Required Qualifications • 7+ years in data engineering/analytics engineering with 4+ years hands-on Palantir Foundry at scale. • Deep expertise in Foundry Ontology, Code Workbooks, Pipelines, Materializations, Lineage/Provenance, and object permissions. • Strong SQL and PySpark/Scala in Foundry; comfort with UDFs, window functions, and partitioning/bucketing strategies. • Proven operational excellence: SLAs/SLOs, alerting, data quality frameworks, backfills, rollbacks, blue/green or canary data releases. • Fluency with Git, CI/CD for Foundry code repos, test automation for transforms, and environment promotion. • Hands-on with cloud storage & compute (AWS/Azure/GCP), file formats (Parquet/Delta), and cost/perf tuning. • Strong grasp of data governance (PII, masking, policy tags) and security models within Foundry. Nice to Have • Building Workshop/Slate UX tied to ontology objects; authoring Actions and integrating AIP use cases. • Streaming/event ingestion patterns (e.g., Kafka/Kinesis) materialized into curated datasets. • Observability stacks (e.g., Datadog/CloudWatch/Prometheus) for pipeline telemetry; FinOps/cost governance. • Experience establishing platform standards: templates, code style, testing frameworks, domain data product catalogs. Success Metrics (90–180 Days) • ≥99.5% pipeline success rate, with documented SLOs and active alerting. • <20% runtime/cost reduction via optimization and materialization strategy. • Zero P1 data incidents and ≤4h MTTR with playbooks and automated remediation. • 3+ reusable templates (ingestion, CDC, enrichment) adopted by partner teams. • Ontology coverage for priority domains with versioned contracts and lineage.