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Data Engineer

Description Docupace
Full-time
Remote
United States
Description

Docupace is seeking a Senior Data Engineer to design, build, and operate the foundational data infrastructure behind our Palantir Foundry platform. This high-impact role is central to enabling analytics, automation, and AI capabilities across our wealth-management ecosystem.


Role Summary 

You will build, model, and operate the data foundations that power our Palantir Foundry platform. This role owns ingestion and transformation across multi-custodian feeds, wealth-management systems, and compliance workflows, and maintains the Foundry Ontology to enable analytics, AI agents, and operational automation. 


Requirements

Must-Have Qualifications 

  • 5+ years in data engineering with distributed processing (Spark/PySpark) and advanced SQL.
  • Hands-on Palantir Foundry experience (≥1 year building pipelines and Ontology) or demonstrably fast ramp with strong DE background on comparable stacks (e.g., Databricks/Spark).
  • Strong proficiency in SQL — Foundry heavily relies on SQL-like operations for data transformations (similar to pipelines in dbt or Snowflake).
  • Experience with data lineage and versioned transformations
  • Proven data modeling across relational and graph/relationship domains.
  • Comfort working with Spark, Hadoop, or other distributed compute engines
  • Knowledge of data partitioning, optimization, and parallelized workloads.
  • API integration (REST/GraphQL), including auth, pagination, retries, and schema change handling.
  • Testing & CI/CD for data (unit tests, data quality checks, code reviews) and strong Git hygiene.
  • Excellent communication with product, compliance, and engineering partners.

Nice-to-Have 

  • Wealth management/fintech or regulated industry experience; familiarity with FINRA/SEC workflows.
  • Multi-custodian data (e.g., Schwab, Fidelity, Pershing), account opening, NIGO resolution, surveillance.
  • AWS data services and Infra-as-Code (e.g., S3/IAM/Glue/Terraform).
  • Streaming (Kafka) and/or document/OCR pipelines for forms and unstructured data.
  • Palantir training/certifications.

30/60/90 Outcomes 

30 days 

  • Access configured; baseline ingestion from ≥2 priority sources landing in curated layers.
  • Draft Ontology v0 (Clients/Accounts/Transactions/Forms) reviewed with Product & Compliance.
  • Initial DQ checks live (completeness, referential integrity).


60 days 

  • Ontology v1 published with relationships and semantic metrics for key use cases (NIGO/NAO, AUM views).
  • 4–6 production pipelines with automated monitoring/alerts and runbooks; lineage documented end-to-end.

90 days 

  • First Workshop app/report and one AI/agent use case unblocked by Ontology actions or features.
  • RBAC and PII controls in place; audit evidence automated for schema changes and releases.
  • Cost/performance baseline with monthly efficiency plan.




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