18+ years of data engineering means we've seen what works and what doesn't. Here's how we approach engagements—and a look at the kinds of problems we've solved across federal, healthcare, finance, and enterprise organizations.
We start by listening. What's broken? What does success look like? We dig into your current state, constraints, and goals before proposing solutions.
Architecture first. We define the end-state data model, platform choices, and governance patterns before writing code—so we're building toward something, not just reacting.
We deliver working pipelines and reports in iterations—not a big-bang go-live. You see progress, validate assumptions, and course-correct early.
We document, train, and transfer knowledge. When we leave, your team owns it—no vendor lock-in, no mystery code, no "call Spider to fix it."
A federal defense agency needed to modernize legacy financial data processing, transforming SAP-backed ERP extracts into governed analytics layers while maintaining audit-ready accuracy across complex financial domains.
Delivered: Unity Catalog–governed Delta Lake pipelines processing $50M+/month in transactions. Implemented Bronze/Silver/Gold medallion architecture with reconciliation logic, match/merge rules, and Power BI dashboards for executive reporting and audit readiness.
A financial services firm had a stalled legacy reporting ecosystem with fragmented SQL, Salesforce, and operational data spread across disconnected systems—causing trust and performance issues across the organization.
Delivered: Databricks + Azure Lakehouse architecture with governed Medallion pipeline. Built secure Gold-layer compensation and financial models consumed by Power BI, with schema redesign, match/merge strategies, and Delta Lake optimization.
A regional healthcare payer needed to modernize a 500GB+ legacy SQL Server warehouse into a cloud-native architecture while maintaining regulatory compliance and operational reporting continuity.
Delivered: Governed Medallion architecture with 50+ dbt models, scripted lineage, and secure ADF orchestration. Integrated with SQL stored procedures and regulatory reporting requirements while optimizing cloud costs.
A national automotive retailer needed to ingest and process real-time vehicle telematics data—location, diagnostics, operational metrics—at scale for fleet analytics and operational monitoring.
Delivered: Structured Streaming pipelines in Databricks with Autoloader-style incremental processing, checkpointing, and schema enforcement. Built fault-tolerant Silver/Gold streaming tables supporting low-latency fleet analytics.
A global IP management platform needed to migrate and consolidate data from complex enterprise systems for Fortune 100 clients, supporting patent, trademark, and licensing analytics at scale.
Delivered: Scalable ETL/ELT pipelines using Azure Databricks and SQL Server. Extended core migration framework to automate key-mapping logic, reducing manual effort by 30%. Achieved 40%+ query performance improvements through optimization.
A geothermal energy company had 18+ years of operational data trapped in 500+ Excel files and legacy SQL—limiting visibility into drilling performance, maintenance planning, and vendor procurement decisions.
Delivered: Modernized from on-prem SQL Server to Azure, unifying historical data into governed Delta schemas. Built a remaining-useful-life (RUL) predictive model for drilling equipment, enabling proactive maintenance and procurement optimization.
Whether it's legacy migration, real-time streaming, lakehouse architecture, or getting your data AI-ready—we've probably solved something like it before. Let's talk about what you're working on.
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