The Role

As Principal Data Platform Engineer, you'll architect and implement our centralized data platform on Databricks. You'll establish governance patterns using Unity Catalog, optimize for cost and performance at scale, and enable our existing Data Engineers to build confidently on the platform. This is a data infrastructure role—focused on pipelines, storage, governance, and platform operations. 

The Business Challenge

We operate multiple product lines (Transfer Pricing, R&D Services, RoyaltyStat, Provisioning), each with distinct databases containing enterprise financial data—journal entries, general ledgers, and financial statements. Our immediate challenge is migrating multi-terabyte datasets from legacy systems to a unified Databricks lakehouse while establishing governance patterns that enable multi-product operations at scale.

What You'll Build

• Data Structuring: Design data models and implement unified schemas across multiple disparate product lines.
• Unity Catalog Architecture: Design and implement multi-catalog governance strategy supporting data isolation, cross-product data sharing, and comprehensive lineage tracking across our product portfolio
• Delta Lake Optimization: Establish patterns for Z-ordering, compaction, and liquid clustering at multi-TB scale. Define table structures, partitioning strategies, and retention policies that balance query performance with storage costs
• ETL Pipeline Framework: Build declarative pipeline patterns using Delta Live Tables. Create orchestration workflows for ingesting data from internal sources such as SQL databases and S3
• Third Party Integrations: Integrate with third party data sources such as ERP systems (Netsuite etc.) and external data providers (S&P etc.) with automated ingest, robust error handling and monitoring.
• Platform Operations: Implement cost monitoring and optimization strategies, establish data quality frameworks, create self-service patterns enabling Data Engineers to work independently while maintaining governance standards

Business Problems You'll Solve

• Key Legacy Product Migrations: Lead the architecture for migrating multi-terabyte datasets from legacy systems to Databricks—establishing patterns that will be reused across multiple product lines
• Multi-Product Data Architecture: Design Unity Catalog structures enabling secure data separation between product lines while allowing controlled cross-product analytics where appropriate
• Cost-Efficient Scale: Build infrastructure that scales efficiently—through intelligent caching, query optimization, and compute management strategies that avoid linear cost growth
• Platform Reliability: Establish monitoring, alerting, and data quality validation ensuring the platform operates reliably as foundation for both analytics and AI workloads

Required Experience

Databricks Expertise (Required)

• Unity Catalog: Production experience with multi-catalog governance, metastore design, and lineage tracking.
• Data Structuring: Experience designing and building unified schemas across multiple disparate product lines.
• Delta Lake: Expert-level experience with Z-ordering, compaction, liquid clustering, and performance tuning at multi-TB scale
• Delta Live Tables: Strong hands-on experience building declarative ETL pipelines, including change data capture and expectations/constraints
• Databricks Workflows: Experience with job orchestration, scheduling, and operational monitoring
• Business Intelligence: Experience enabling company-wide analytics and reporting with modern business intelligence tools and maintaining source of truth data and metrics.
• PySpark & Databricks SQL: Strong proficiency for code review, performance tuning, and query optimization

Core Platform Engineering

• 5-8 years in data engineering or data platform roles, with 3+ years hands-on Databricks experience
• Track record leading at least one significant platform build or migration project
• AWS experience (S3, IAM, VPC) with ability to collaborate on infrastructure decisions
• Infrastructure-as-code experience (Terraform preferred)

Technical Leadership

• Demonstrated ability architecting data platforms from first principles and defending technical decisions
• Strong written and verbal communication— document architecture decisions and present to both technical and business stakeholders

Preferred But Not Required

• Experience with financial data, accounting systems (NetSuite), or enterprise ERP platforms
• Background building platforms that serve AI/ML workloads (experience preparing data for downstream ML consumption, RAG and retrieval, and LLMs.
• Understand advanced intelligence concepts such as relationship surfacing with knowledge graphs
• Familiarity with data governance frameworks and compliance requirements for regulated industries