Senior Data Analyst, Energy Preconstruction
About The Position
Moss is seeking a Senior Data Analyst for its Energy Preconstruction team. This role is crucial for transforming project data (engineering, cost, productivity, procurement, performance) into actionable insights to improve estimating accuracy, mitigate risk, and boost profitability. The Senior Data Analyst will develop a historical dataset for benchmarking, conceptual pricing, forecasting, and strategic decision-making. Working with minimal supervision, this individual will collaborate with preconstruction, procurement, finance, engineering, and IT to establish a unified source of truth for preconstruction data. Key activities include data cleansing, querying multiple systems, developing dashboards, and performing business analysis to enhance bid strategy, estimate confidence, identify cost/risk patterns, and support the growth of a data function within Energy Preconstruction.
Requirements
- Bachelor’s degree in Data Analytics, Data Science, Engineering, Finance, Information Systems, or a related field.
- 5+ years of experience in data analytics or a related analytical role.
- Strong experience in cleansing, standardizing, and structuring complex datasets.
- Strong SQL proficiency and experience querying databases.
- Strong Excel proficiency; advanced Excel skills, including Power Query, PivotTables, and structured data manipulation, are preferred.
- Strong experience building dashboards and reports in Power BI or a similar tool.
- Experience in identifying correlations, patterns, and trends in data to support business decisions.
- Experience working independently and collaborating across business and technical functions.
- Experience supporting data governance, standardization, or system integration efforts.
- Knowledge of data quality, database concepts, query logic, enterprise data environments, dashboarding, KPI development, benchmarking, correlation analysis, trend analysis, and forecasting.
- Strong analytical, problem-solving, documentation, communication, and stakeholder collaboration skills.
Nice To Haves
- Experience in energy, EPC, construction, or infrastructure environments.
- Experience with ERP systems, such as Oracle, and CRM systems.
- Experience with Python or R for data analysis or automation.
- Familiarity with estimating, engineering, and procurement workflows.
Responsibilities
- Cleanse, normalize, validate, and consolidate historical data on estimating, engineering, cost, productivity, procurement, project parameters, and project performance from various sources like spreadsheets, takeoff files, ERP/CRM systems, and legacy systems.
- Build, maintain, and improve structured historical datasets and databases to support estimating benchmarks, conceptual pricing, root cause analysis, and predictive modeling.
- Enhance data quality and usability by resolving inconsistencies in naming conventions, units of measure, metadata, assumptions, and source traceability.
- Build and run queries against internal databases and enterprise systems, using SQL and other tools to extract, join, filter, validate, and organize data from multiple sources.
- Develop repeatable query logic and data pipelines to improve data accessibility, consistency, and auditability, collaborating with IT and data teams on governance standards and future data architecture.
- Identify correlations, trends, anomalies, and performance patterns across historical and active energy projects, analyzing relationships between design variables, cost drivers, labor productivity, procurement timing, geography, weather, and project outcomes.
- Generate insights to improve profitability, reduce risk, strengthen conceptual estimates, and support value engineering and broader business decision-making.
- Benchmark current bids and conceptual estimates against historical project performance, market trends, prior wins, and known cost drivers; support pricing and repricing exercises through structured data analysis.
- Build dashboards, reports, and KPI visibility tools using Power BI or similar platforms to track estimate accuracy, cost variance, margin trends, bid competitiveness, win rates, and project milestones.
- Translate complex analysis into clear, decision-oriented reporting for leadership and business stakeholders.
- Support risk analysis, forecasting, sensitivity analysis, scenario modeling, contingency planning, and feasibility analysis using internal and external data.
- Support the improvement and standardization of estimating and engineering templates, define and reinforce data standards, act as a technical liaison across estimating, engineering, procurement, finance, and IT, and contribute to continuous improvement and future system integration.
- Perform other duties as assigned.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
