Engineering Data Migration & Refinement
Enterprise data refinement and migration workflow supporting consolidation of engineering document systems into Hexagon SDx. Designed and executed large-scale metadata validation, transformation, and loadsheet generation using Excel, Python, and SQL to ensure data integrity across global sites.
Type: Enterprise Data Workflow & System Migration
Industry: Chemical Manufacturing
Overview
Served as the data engineer on the Asset Information Management (AIM) initiative of a Top 10 U.S. chemical producer — a multi-site program to consolidate fragmented engineering document systems into the corporate-wide Hexagon SDx platform.
The initiative focused on migrating and standardizing engineering drawings and associated metadata for active chemical facilities, including plot plans, equipment drawings (e.g., distillation towers, heat exchangers, reactors), building layouts, and other critical asset documentation.
My role was to design and own a structured “data refinement” workflow to measure, validate, transform, and prepare large volumes of legacy engineering document metadata for reliable SDx ingestion.
The Challenge
Historically, each chemical site operated independently with its own document management tools, naming conventions, and governance practices. Over time, this led to:
- Inconsistent document naming structures
- Duplicate and outdated drawing files across repositories
- Missing or incomplete metadata classifications
- Invalid or non-standard unit identifiers
- Weak revision tracking practices
- Fragmented storage across legacy systems and shared network drives
- Misalignment with SDx naming and metadata standards
Because these drawings support active operating facilities, data integrity and version control were critical. Migrating inconsistent metadata directly into SDx would have created long-term reliability and compliance risks.
The Solution
I created and owned the end-to-end data refinement workflow using Excel, Python, and SQL.
This structured process included:
- Developing validation queries to measure data quality and identify scope gaps
- Standardizing legacy naming conventions to align with SDx requirements
- Identifying and resolving duplicate and revision inconsistencies
- Correcting invalid unit numbers and metadata mismatches
- Generating structured SDx loadsheets for ingestion
- Performing bulk uploads to support milestone-driven releases
- Delivering actionable data quality insights to the Product Owner and agile team
Rather than simply migrating data, the workflow improved it — transforming fragmented legacy information into a governed, standardized asset information foundation.
Impact
The refinement and migration process ensured that SDx became a trusted, searchable, and scalable engineering document platform.
The AIM initiative positioned the client for:
- Improved searchability and faster document retrieval
- Stronger revision control and reduced risk of outdated drawing use
- Enhanced data integrity and regulatory readiness
- Reduced rework during system integration
- Long-term scalability and centralized governance across sites
This project combined enterprise data engineering, workflow design, and systems integration to support a critical digital transformation initiative.