Case Study

Enterprise Analytics Modernization

Designed and implemented a centralized, enterprise-grade analytics platform to consolidate operational, engineering, and financial data from siloed source systems into a single, governed Snowflake data warehouse capable of supporting regulatory reporting, operational decision-making, and long-term analytics modernization.

Data Warehouse Snowflake Enterprise Analytics Data Governance Regulatory Compliance Python / SQL

Executive Snapshot

Objective
Design and implement a centralized, enterprise-grade analytics platform to consolidate operational, engineering, and financial data from siloed source systems into a single, governed data warehouse capable of supporting regulatory reporting, operational decision-making, and long-term analytics modernization.
My Role
Technical Lead and hands-on architect. Owned platform design, data architecture, ingestion pipelines, validation strategy, and migration execution. Led change management and analyst enablement efforts, working directly with business stakeholders, analysts, regulators, and local government representatives.
Scope
Enterprise-wide analytics modernization spanning operational telemetry, engineering systems, maintenance platforms, and financial data sources. Migrated legacy reporting logic and business-critical metrics into Snowflake.
Scale
Several million new records ingested daily • Tens of thousands of physical assets • Thousands of internal users • Regulatory and royalty-related reporting
Impact
Scalable, trusted analytics foundation that standardized data structures, improved data quality, enabled near real-time insights, and supported regulatory and government-facing reporting.
Data Ingestion Pipelines Normalized Data Model Regulatory-Grade Validation Near Real-Time Analytics Change Management Analyst Enablement

Outcome

  • Achieved ~40% reduction in manual data preparation and reporting effort through automated pipelines and standardized data models.
  • Delivered regulatory-grade accuracy for compliance and royalty reporting, with improved confidence from internal stakeholders and external government entities.
  • Established enterprise-wide standardization of KPIs and reporting logic across thousands of users.
  • Created foundation for future predictive analytics and machine learning initiatives.
The platform replaced fragmented legacy solutions with a single source of truth and positioned the organization for future analytics modernization.

Problem

Prior to this initiative, analytics and reporting were fragmented across teams and systems. Business logic was duplicated, reports were manually maintained, and data quality varied by source and consumer. Regulatory data, including injection pressures and production metrics, required high accuracy but lacked consistent validation processes. Stakeholders lacked confidence in the reliability and auditability of analytics outputs, limiting scalability and modernization efforts.

Solution

  • Designed a scalable, source-agnostic Snowflake data warehouse to serve as the enterprise analytics backbone.
  • Implemented standardized ingestion and transformation pipelines across operational, engineering, maintenance, and financial systems.
  • Developed a rigorous data validation framework to ensure accuracy at scale.
  • Migrated and rationalized legacy reporting logic into governed, analytics-ready data models.
  • Enabled near real-time operational visibility and executive reporting.
  • Led analyst onboarding, training, and adoption to ensure business continuity and trust.

System Capabilities & Functional Highlights

Capability Description Business Impact
Unified Data Ingestion Framework Standardized pipelines supported high-volume batch ingestion and operational telemetry, enabling consistent onboarding of new source systems while reducing custom development and maintenance overhead. Reduced development overhead and enabled rapid onboarding of new data sources
Normalized Enterprise Data Model A shared, normalized data model preserved business and regulatory logic while enabling consistent reporting across teams and domains. Consistent reporting across all business functions and teams
Regulatory-Grade Data Validation Python-based Jupyter notebooks queried both source systems and Snowflake target, generating row counts, sums, averages, and aggregate comparisons to verify completeness and accuracy, flagging discrepancies automatically. High confidence while processing millions of daily records with regulatory compliance
Operational & Engineering Analytics Near real-time analytics provided visibility into production performance, asset health, and injection activity, supporting both operational decision-making and regulatory reporting requirements. Near real-time insights for operational decision-making and regulatory compliance

Validation, Quality Assurance & Compliance

Parallel Validation Strategy

Data accuracy was validated through multiple layers:

  • Programmatic validation using Python scripts comparing source and target datasets
  • Aggregate checks (counts, sums, averages) to detect drift or loss
  • Analyst-led visual validation, where business users compared new reports to legacy outputs
Regulatory & Government Assurance

Certain datasets, including injection pressure data, were subject to strict regulatory oversight. I worked directly with internal compliance teams and local government representatives to demonstrate the reliability, accuracy, and auditability of the new analytics environment. This was critical because production data directly impacted royalty payments to the city, requiring a high degree of trust and transparency.

Analyst Enablement & Change Management

Training & Adoption

I led the change management effort by coordinating analyst training and onboarding. Analysts were guided on:

  • Where to access new reports and datasets
  • How to interpret standardized metrics
  • How the new platform improved accuracy and timeliness

Analysts also played an active role in validation by reviewing reports visually and confirming alignment with prior outputs, accelerating trust and adoption. The transition minimized disruption, ensured continuity of business-critical reporting, and resulted in strong adoption across operational and analytical teams.

Business Impact & Measurable Outcomes

Operational Efficiency
  • ~40% reduction in manual data preparation and reporting effort
  • Near real-time availability of operational and engineering data
  • Faster identification of anomalies and performance issues
Data Trust & Governance
  • Regulatory-grade accuracy for compliance and royalty reporting
  • Improved confidence from internal stakeholders and external government entities
  • Reduced reconciliation effort and reporting disputes
Adoption & Scale
  • Thousands of users across multiple business functions
  • Enterprise-wide standardization of KPIs and reporting logic
  • Foundation established for future predictive analytics and machine learning

Leadership & Collaboration

Technical Leadership

Owned architecture, ingestion design, validation strategy, and implementation decisions.

Stakeholder Partnership

Collaborated closely with analysts, business leaders, compliance teams, and regulators.

Strategic Alignment

Ensured platform design aligned with long-term analytics modernization goals.

  • Operational Systems: XSPOC, ProCount
  • Databases: PostgreSQL, SQL Server
  • Maintenance Systems: Emaint
  • Data Platform: Snowflake
  • Integration Tools: Workato, Fivetran
  • Custom Services: C# microservices for ingestion and orchestration
  • Validation & Analytics: Python (Jupyter), SQL
  • Reporting & BI: Tableau, Power BI, Spotfire
  • Data Platform: Snowflake Data Warehouse
  • Validation Framework: Python-based Jupyter notebooks for data quality assurance