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

The Role

As Staff Data Engineer, you will provide senior onshore technical leadership for the data engineering team. You will own a defined slice of our centralized Databricks data platform with full accountability for decisions and delivery, serve as a technical counterpart to the Principal Data Platform Engineer, and drive architectural judgment and independent problem-solving as platform complexity scales post-migration.

This is a hands-on data engineering role focused on building and maintaining production pipelines, exercising architectural judgment on data modeling and pipeline design, and serving as the onshore escalation point and institutional knowledge backup for platform decisions.

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. As the platform matures, the data engineering team needs senior onshore technical presence to drive architecture ownership and maintain platform quality.

What You'll Build

• Production Data Pipelines: Build and maintain production data pipelines within the patterns and governance established by the Lead Data Platform Engineer, ensuring reliability and performance at multi-terabyte scale.
• Data Modeling & Architecture: Exercise architectural judgment on data modeling, pipeline design, and platform usage—translating complex business requirements into scalable data solutions across our product portfolio.
• Stakeholder Engagement: Engage proactively with product and engineering stakeholders to translate requirements into data solutions, serving as the primary onshore technical point of contact for data engineering needs.
• Platform Quality: Drive platform quality through code reviews, testing practices, and engineering standards that ensure the team delivers reliable, maintainable data infrastructure.
• Knowledge & Continuity: Serve as onshore escalation point and institutional knowledge backup for platform decisions, reducing single-point-of-failure risk and building onshore technical depth as the platform scales.

Business Problems You'll Solve

• Multi-Product Data Delivery: Implement data pipelines that serve multiple product lines (Transfer Pricing, R&D Services, RoyaltyStat, Provisioning) with distinct data requirements, ensuring each product gets the data it needs reliably and on schedule.
• Legacy Migration Execution: Lead pipeline implementation for migrating multi-terabyte datasets from legacy systems to Databricks, working within the architecture defined by the Lead Data Platform Engineer.
• Onshore Technical Leadership: Provide the senior judgment layer the current nearshore team cannot—owning problems end-to-end, making independent architectural decisions, and mentoring engineers to raise the quality bar across the team.
• Cross-Team Coordination: Bridge the gap between product teams and data infrastructure, translating business requirements into data solutions and ensuring the data platform delivers on product commitments.

Required Experience

Core Data Engineering

• SQL, Python, and PySpark—production pipeline implementation and performance optimization
• Databricks experience—Delta Lake, Workflows, and Databricks SQL; Unity Catalog familiarity preferred
• 5+ years in data engineering with demonstrated ability to own problems end-to-end without close direction
• Experience building and maintaining ETL/ELT pipelines at scale, including error handling, monitoring, and data quality validation
• Strong data modeling skills across structured and semi-structured data sources

Platform & Infrastructure

• AWS experience (S3, IAM, VPC) with ability to collaborate on infrastructure decisions
• Infrastructure-as-code experience (Terraform preferred)
• Familiarity with data governance patterns (Unity Catalog, data lineage, access controls)

Technical Leadership

• Demonstrated ability to exercise independent architectural judgment—not just ticket execution
• Experience mentoring or guiding junior and mid-level data engineers
• Strong written and verbal communication—able to document architecture decisions and engage directly with both technical and business stakeholders
• Onshore (US-based)—role requires timezone overlap, async-light communication, and direct stakeholder engagement

Preferred But Not Required

• Experience with financial data, accounting systems (NetSuite), or enterprise ERP platforms
• Background building pipelines that serve AI/ML workloads (preparing data for downstream ML consumption, RAG, and LLMs)
• Familiarity with data governance frameworks and compliance requirements for regulated industries
• Experience working alongside or transitioning from nearshore engineering teams

What We Offer

(The following only applies to US-based positions)

• A collaborative team culture with opportunities for career development.
• Ample opportunities to be recognized, build valuable skills, and grow your career.
• Generous vacation policy, including paid parental leave.
• Comprehensive health plans with FSA and HSA options.
• 401(k) retirement plan.
• Life and disability insurance coverage.
• Supplemental benefits like a dependent care savings plan, pet insurance, will preparation, and an employee assistance program.

About Us

At Exactera, a FinTech SaaS start-up founded in 2016, we stand at the intersection of human and machine intelligence. Our corporate tax solutions are powered by AI and cloud-based technologies, serving customers worldwide. We are committed to diversity, inclusion, and equal opportunities for all.