Juan Manuel Díaz Vega

I’m a Data Analyst with 11+ years of engineering and operations experience, specializing in statistical analysis, ETL automation, and executive BI reporting in global manufacturing environments. I have a proven track record of transforming raw machine logs and operational data into measurable financial impact, including EUR 670K and EUR 50K+ in annual savings. I’m experienced in building asynchronous data deliverables, communicating findings to VP-level leadership, and leading cross-functional remote/international teams. I’m currently completing the TripleTen Data Analytics Bootcamp.

Technical toolkit: SQL (CTEs, joins, aggregations), Python (Pandas, NumPy, EDA), data cleaning and statistical analysis (DOE, Gage R&R/MSA), Power BI, Tableau, Power Query (ETL), executive dashboards, and advanced Excel. Process improvement background in DMAIC, Pareto analysis, root cause analysis (Ishikawa), Lean Manufacturing, and Six Sigma Green Belt.

As Operational Engineering & Maintenance Manager at Huf Mexico (2022-present), I analyze daily KPI data across 23 production programs using automated Power BI dashboards, driving an 18% increase in OEE while managing remote and on-site teams. I led root-cause analysis and Design of Experiments (DOE) on laser-welding scrap that uncovered process anomalies worth EUR 50K+ in annual savings, presented to the Global VP of Engineering. I also engineered automated Power Query ETL pipelines processing 9,000+ daily machine log records, enabling real-time process monitoring and cutting manual reporting effort by 90%, and applied statistical failure-mode analysis to build predictive maintenance schedules that cut critical failures by 15% and downtime by 12%.

Previously, as Engineering Coordinator (2018-2021), I modeled test-bench cycle time data using Pareto analysis and MSA, optimizing end-of-line testing from 7.5 to 5.6 seconds and avoiding a EUR 670K secondary production line investment while boosting OEE by 3%. I also collaborated asynchronously with international cross-functional teams to launch 6 production lines, standardizing data reporting and cutting implementation time by 20%. As an Industrial Engineer (2015-2018), I built statistical data models and Lean tracking templates that reduced line scrap by 15% and improved material yield.

I hold a B.S. in Mechatronic Engineering and I’m looking for a remote Data Analyst role where I can apply this blend of statistical rigor, automation, and operational leadership to help teams make faster, data-driven decisions.