Palantir Foundry Engineer - Nashville TN (Onsite Day one)
Tennessee
Role - Palantir Foundry Engineer
Domain - (Healthcare Domain)
Location - 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/Google Cloud Platform), 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.
Example Work You ll Own
Stand up incremental CDC pipelines with watermarking & late-arrivals handling; backfill historical data safely.
Define business-ready ontology for a domain and wire it to Workshop apps and AIP agents that trigger Actions.
Implement DQ gates (null/dup checks, distribution drift) that fail fast and auto-open incidents with context.
Build promotion workflows (dev staging prod) with automated tests on transforms and compatibility checks for ontology changes.
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