Role: Databricks Architect
Duration: Fulltime
Location: Charlotte, NC (Onsite)
ABOUT THE ROLE
Role Overview
Service Logic is investing in a modern, cloud-native data platform to bring enterprise-wide financial
visibility across its growing portfolio of business units and ERP systems. At the centre of this investment is
Spend 360 a cross-ERP spend analytics programme that consolidates Accounts Payable, purchase
orders, vendor data, invoice detail, and general ledger information across more than a dozen source
systems into a single governed analytical model.
We are looking for an experienced Data Engineer who combines deep Databricks and data warehousing
expertise with genuine financial domain knowledge. The right person understands how financial data flows
from an ERP transaction to a reported number, knows how to build the systems that make that journey
reliable and auditable, and can design the architecture that scales it as the business grows.
WHAT YOU WILL DO
Key Responsibilities
Design and build the Spend 360 data platform. Own the end-to-end architecture and implementation of the medallion lakehouse Bronze ingestion from multiple ERP systems, Silver cleansing and cross-system harmonisation, and Gold-layer conformed dimensional models powering financial reporting and analytics.
Integrate and harmonise multi-ERP source data. Work directly with WennSoft, Sage, Sampro, QuickBooks, Jonas, Microsoft Dynamics, and others. Build ingestion pipelines that handle each system's data structures, extract mechanisms, and quirks reliably.
Build and maintain financial reconciliation logic. Design and implement reconciliation checks that validate AP transactions, invoice line items, PO commitments, GL postings, and vendor balances against source systems and reporting outputs.
Design and implement vendor and master data management. Build the cross-ERP entity resolution layer fuzzy matching, confidence scoring, and human-review workflows for data stewards.
Develop real-time and near-real-time processing capabilities. Architect streaming or micro-batch pipelines for time-sensitive financial data invoice aging, open commitment tracking, and cash flow visibility.
Establish data quality and governance standards. Implement Delta constraints, DLT expectations, and reconciliation checks that enforce referential integrity and catch issues before data reaches reporting consumers.
Partner with finance and operations stakeholders. Translate financial reporting requirements into data model and pipeline specifications. Facilitate UAT with finance teams and ensure reported numbers are trusted.
Support and extend analytical and reporting systems. Build semantic layer definitions, Power BI / Databricks AI/BI dataset structures, and dbt metric definitions for consistent, single-version financial KPIs.
REQUIRED QUALIFICATIONS
What We Are Looking For
Databricks and Cloud Data Platform
Databricks Delta Lake
Delta Live
Tables Unity Catalog PySpark Lakeflow
Proven hands-on experience designing and building data pipelines on Databricks architecting the full medallion pattern (Bronze / Silver / Gold) from first principles.
Deep understanding of Delta Lake time travel, MERGE operations, SCD Type 2, Z-ordering, and Delta constraints for data quality enforcement.
Experience with Unity Catalog for governance, lineage tracking, and column-level security.
Data Warehousing, ETL and ELT Architecture
Dimensional
modelling Star schema SCD Type 2 ETL/ELT design CDC
Incremental
processing
Strong foundation in dimensional data warehousing fact and dimension design, conformed dimensions, slowly changing dimensions, and grain selection.
Experience designing ELT pipelines at scale incremental load patterns, watermark-based CDC, idempotent pipeline design, and reconciliation between source and warehouse counts.
Familiarity with semantic layer frameworks such as dbt, Power BI datasets, or equivalent.
ERP Systems Knowledge
WennSoft / Dynamics GP Sage 100 / 300 QuickBooks Sampro Jonas Multi-ERP integration
Working knowledge of at least two ERP systems and their underlying data structures: WennSoft / Microsoft Dynamics GP, Sage 100 or 300, QuickBooks, Sampro, Jonas, or similar field service / HVAC / construction ERP platforms.
Understanding of how ERP systems structure AP, PO, job cost, GL, and vendor master data and the quirks that make cross-ERP integration non-trivial.
Experience connecting to ERP source systems via direct database access, API, or CDC tooling (DataForge, Fivetran, or equivalent).
Financial Reporting and Reconciliation
Accounts
Payable
Invoice
matching
GL
reconciliation
Purchase
orders 3-way match Financial close
Understanding of core financial processes AP lifecycle (invoice receipt, approval, posting, payment), PO commitment accounting, GL account structure, and financial close cycles.
Ability to design and implement automated reconciliation checks: AP sub-ledger to GL, invoice lines to PO lines (3-way match), spend actuals to budget, and reported figures to source system totals.
Comfortable tracing a discrepancy from a dashboard figure back to a source transaction and explaining it in terms a finance analyst understands.
Real-Time and Near-Real-Time Processing
Structured Streaming Event-driven pipelines Micro-batch processing Low-latency delivery
Experience designing backend systems for time-sensitive data processing streaming or micro-batch pipelines that keep open commitment figures, invoice aging, and cash position fresh.
Understanding of when real-time processing is warranted versus when a well-designed incremental batch pipeline is the right trade-off.
PREFERRED QUALIFICATIONS
Nice to Have
These are advantages, not requirements
Experience in field service, HVAC, mechanical, or construction industries familiarity with job cost accounting, work order management, and service contract revenue recognition.
Experience with AlixPartners or similar spend taxonomy frameworks (UNSPSC, commodity coding).
Hands-on experience with DataForge for multi-source ERP ingestion.
Familiarity with Power BI, Databricks AI/BI, or Tableau for semantic layer and report development.
Experience implementing vendor or customer MDM entity resolution, deduplication, and golden record creation across heterogeneous source systems.
Exposure to M&A; data integration onboarding newly acquired business units with different ERP systems.
Working knowledge of accounts receivable, revenue recognition, or financial forecasting data structures.
WHAT SUCCESS LOOKS LIKE
In Your First Year
You will have succeeded when:
The Spend 360 dimensional model is in production, trusted by finance teams, and reconciled against source AP and GL balances with documented audit trails.
New business units and ERP systems can be onboarded to the platform without new notebook deployments added via configuration, not code.
Vendor master data is harmonised across all active ERP systems through a governed, human-reviewed mastering process one canonical vendor record per real-world supplier.
Finance teams can answer "what did we spend with this vendor across all business units" and "what is our open commitment against this job" from a single report, with confidence.
The platform is extensible the architecture powering Spend 360 is ready to onboard Revenue Flash, AR analytics, and future financial domains without re-architecture.
