Data Analyst

Wood (trading locally as WOOD KSS LLP) is looking to hire Data Analyst (KZ Passport holders) with extensive experience in the oil and gas petrochemical industry to join our Operations business, based in Atyrau, Kazakhstan.

• Maintain traceability and consistency between project documents, databases, and cutover tracking tools.
• Perform cross-checks and reconciliation between Load Lists, Cable Schedules, Cable Splicing Schedules, MCC Load Lists, Equipment Registers, Instrument Indexes, Engineering Databases, Turnover Packages, and Cutover Tracking Databases.
• Identify missing, duplicate, inconsistent, or conflicting records.
• Establish and maintain a single source of truth for project data
• Develop and execute automated validation processes to verify tag consistency across project documentation.
• Identify duplicate records, conflicting data, missing records, and orphaned records.
• Validate equipment, cable, feeder, source, and destination relationships.
• Verify MCC feeder assignments and load mappings.
• Ensure consistency between engineering databases and project deliverables.
• Generate exception reports highlighting discrepancies requiring engineering review.
• Support data readiness assessments for cutover execution

Develop and maintain advanced Excel-based tools to support data validation, reconciliation, reporting, and cutover readiness activities, including:

• Advanced Excel models for consolidation and analysis of large project datasets.
• Power Query solutions for automated extraction, transformation, and consolidation of data from multiple sources.
• Power Pivot models for analysis of complex relationships between engineering datasets.
• VBA-based validation tools to automate repetitive checks and exception reporting.
• Automated reconciliation reports to identify missing, duplicate, inconsistent, or conflicting records.
• Dashboard development providing visibility of data quality, cutover readiness, and project progress.
• Custom validation tools for Load Lists, Cable Schedules, Cable Splicing Schedules, MCC Load Lists, Equipment Registers, Turnover Databases, and Cutover Databases.
• Configurable templates enabling Project Teams to perform self-validation of engineering data before submission to the Cutover Team.
• Develop automated tools capable of processing and validating large drawing packages and associated datasets.
• Compare equipment and cable tags used in drawings against master databases.
• Identify missing, duplicated, or inconsistent tags across drawings, schedules, and engineering registers.
• Validate document cross-references and linked datasets.
• Compare drawing revisions and identify changes that may impact construction, commissioning, and cutover activities.
• Generate discrepancy reports and engineering punch lists based on bulk drawing-package checks.
• Perform automated consistency checks across multiple engineering deliverables
• Develop dashboards and project performance metrics.
• Monitor and report data quality KPIs.
• Prepare management reports and exception summaries.
• Support cutover readiness reviews with data-driven reporting.
• Maintain visibility of outstanding data discrepancies and corrective actions.
• Identify opportunities to eliminate manual data validation activities.
• Develop reusable tools and templates applicable across multiple project workstreams.
• Standardize data validation methodologies and reporting practices.
• Improve efficiency and accuracy of project data management processes.
• Support digitalization and automation initiatives within the Project Team.
• Knowledge of electrical engineering documentation and equipment tagging systems.
• Familiarity with Load Lists, MCC Load Lists, Cable Schedules, Cable Splicing Schedules, and Turnover Packages.
• Experience supporting commissioning, system completion, cutover, or execution-readiness activities
• XLOOKUP, INDEX/MATCH, FILTER, UNIQUE, LET, LAMBDA, Dynamic Arrays, Pivot Tables, Data Models, Power Query, Power Pivot, VBA, Power BI, Power Automate, SQL, SharePoint, and dashboard development
• Ability to work with large engineering datasets, manage relationships between registers, and maintain traceability across source documents
• Structured, detail-oriented, able to work independently, and capable of converting repeated manual checks into reusable tools and templates