Otto Montoya — Data Analyst
Clear insights from complex data. End-to-end across the BI stack.
I'm a Data Analyst with a Bachelor's in Informatics Engineering and Digital Business. From understanding business questions and defining KPIs, to designing dashboards, to building the governance models that keep data secure and trustworthy.
I specialize in Tableau and Amazon QuickSight, and I've built interactive reporting and internal tools with Retool. The projects here span cybersecurity, automotive, non-profit, energy, SaaS, and manufacturing.
What I enjoy most is translating messy requirements into clean data models and intuitive dashboards — and working closely with stakeholders so the result actually fits their workflow.
How I work
- I gather requirements through working sessions and daily syncs — high-level ideas into concrete specs.
- I validate every metric against source systems before a dashboard goes to production.
- I write documentation and run demos so teams can actually use what I build.
- I adapt quickly to new industries — the data problems are often more similar than they appear.
Career highlights
- 696→12 Access paths consolidated — for one Tableau deployment
- 6 Industries served — cybersecurity · automotive · non-profit · energy · SaaS · manufacturing
- ~15 Dashboards built from sketches — for a global automotive manufacturer
- 3+ Years end-to-end BI — from KPI to delivery
Skills
From business question to trusted decision.
Discover — Find the decision behind the request.
I turn ambiguous asks into a shared understanding of the business question, the audience, and the workflow the answer needs to support.
- Requirements discovery
- Stakeholder working sessions
- Source-system analysis
Define — Make the metric unambiguous.
I shape KPIs with business and technical teams, document their logic, and define what a trustworthy result must look like before implementation starts.
- KPI definition
- Metric logic
- Acceptance criteria
Model & govern — Build a trustworthy data foundation.
I translate reporting needs into models and specifications, validate outputs against source systems, and design access rules that remain secure at scale.
- Data modeling
- Governance & RLS
- Data quality & validation
Design & build — Turn complexity into a useful interface.
I structure information, choose the right visual form, and implement production dashboards within the real constraints of Tableau, QuickSight, and Power BI.
- Dashboard UX
- Information hierarchy
- Native BI implementation
Enable — Make the work stick.
I carry delivery through documentation, live demos, training, and iteration so teams understand the result and can confidently use it in their workflow.
- Documentation
- Walkthroughs & training
- Adoption support
Technical toolkit
- BI platforms: Tableau, Tableau Online, Amazon QuickSight, Power BI, Retool
- Analytics engineering: SQL, dbt, Python, Pandas, Jupyter, ETL pipelines
- Data platforms: Snowflake, Amazon Athena, Amazon S3, Neon, Azure ETL
- Delivery & integration: Excel-based BI, Dashboard embedding, Tableau Extensions, Laravel / PHP
Areas of expertise: Data governance, Analytics engineering, Dashboard design, Stakeholder collaboration, Data quality
6 Projects Across 6 Industries
Governed Data Access at Scale — Global Cybersecurity Platform
Industry: cybersecurity | Role: Data Analyst & BI Specialist | Tools: Tableau, Snowflake, dbt | ~12 months | End-to-end
Redesigned Tableau row-level security for a 1,500-user environment, consolidating 696 ad-hoc access paths into 12 maintainable access groups and retiring every identified PII-exposed data source.
The Tableau environment had grown organically over years, leaving 696 ad-hoc access paths across ~1,500 active users, multiple data sources with PII exposure, and roughly 30 team-specific folders that made governance nearly impossible.
Working from a usage and risk analysis of the site, I designed a new group-based Row Level Security model that collapsed 696 access paths into 12 access groups (3 default cases and 9 special cases) and retired 100% of the identified risky data sources. Every active user was migrated to the new model with no loss of legitimate access.
Alongside the RLS redesign, I restructured ~30 team folders into 5 global, access-regulated folders, clearly separating Sandbox, Production, and Staging environments. I defined metrics for stale vs. active content and for inactive users and orphaned assets, then consolidated several manual review processes into a single admin monitoring dashboard, cutting ongoing admin workload by approximately 70%.
Evidence: 696 → 12 — Access paths → groups.
Responsibilities
- Designed a group-based RLS model collapsing 696 access paths into 12 access groups across ~1,500 users
- Migrated every active user to the new RLS model with no loss of legitimate access
- Retired 100% of identified PII-exposed data sources
- Restructured ~30 team folders into 5 global, access-regulated folders separating Sandbox, Production, and Staging
- Maintained the data catalog with dbt tagging and Snowflake metadata to support RLS enforcement
- Unified multiple manual admin review processes into a single Tableau monitoring dashboard for stale content, inactive users, and orphaned assets
- Authored governance documentation, admin demos, and user onboarding materials to roll the model out across all teams
Impact: 696 ad-hoc access paths consolidated into 12 access groups across ~1,500 users. 100% of identified PII-exposed data sources removed. ~70% reduction in ongoing admin workload through process unification.
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Market Research Dashboards for a Global Automotive Manufacturer — Leading German Automotive Manufacturer
Industry: automotive | Role: BI Developer | Tools: Tableau | 6 mo | Implementation
Built the Tableau visualization layer of a custom market research analytics platform, delivering ~15 dashboards from sketch-based designs that surface customer insights by market segment.
The client, one of the largest globally recognized German automotive manufacturers, commissioned a custom market research analytics platform to uncover why specific customer segments prefer particular products and inform targeted marketing and production strategies. The platform combined automated data ingestion from sources like SPSS files, a research-oriented data warehouse supporting longitudinal analysis, and cross-tabulation tools for exploring behavioral patterns and trends. My work covered the visualization layer.
I translated approximately 15 sketch-based designs into fully functional Tableau dashboards, working with datasets prepared by the data engineering team for each market research use case. Each dashboard went through iterative review cycles where I submitted completed work, the client provided feedback, and I refined until function and visuals matched both the original sketches and the client's expectations. The dashboards were then embedded into the client's internal portal as part of the platform's enterprise integration.
In parallel, I conducted R&D on Tableau Extensions to evaluate how third-party visual components could accelerate future development, offer richer chart types, and reduce reliance on complex calculated fields, simplifying both knowledge transfer and long-term maintenance.
Evidence: ~15 — Dashboards delivered.
Responsibilities
- Built the Tableau visualization layer for a custom market research analytics platform
- Translated ~15 sketch-based designs into production Tableau dashboards end-to-end
- Worked with datasets prepared by the data engineering team across multiple market research use cases
- Iterated through multiple client review cycles to align function and visuals with expectations
- Researched Tableau Extensions to expand visual options and simplify long-term maintenance
- Coordinated with the embedding team on technical constraints and integration readiness
Impact: Delivered the full dashboard suite for the platform, live and embedded in the client's internal portal, enabling cross-tabulation and longitudinal analysis of customer behavior to support targeted marketing and production decisions.
Case study: https://www.stxnext.com/case-study/market-research-platform
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BI Layer for an Open Source Foundation's Salesforce Migration — Major Open Source Software Foundation
Industry: non-profit | Role: BI Consultant | Tools: Amazon QuickSight, Snowflake | 3 mo | End-to-end
Designed and built three QuickSight dashboards on a Snowflake-backed data layer covering CRM, web analytics, and GitHub community activity, all redesigned from scratch as part of the client's move off Salesforce reporting.
The client, a major open source software foundation, was rebuilding their analytics and reporting away from Salesforce to gain full ownership of their data and reduce platform dependency. The data engineering team migrated source systems into Snowflake, and I was brought in to build the QuickSight dashboard layer that surfaced that data back to the business.
Working from high-level requirements rather than detailed mockups, I designed and iterated on dashboard concepts, presented them in weekly client reviews, and translated feedback into three production QuickSight dashboards covering Salesforce CRM, web analytics, and GitHub community activity. All three were designed from scratch. For the CRM and web analytics views, existing reports served as reference material for understanding which KPIs the team tracked, but the visualizations, structure, and chart choices were my own design, switched out to be clearer and more concise. The GitHub dashboard had no reference to anchor against and was designed end-to-end from client requirements alone.
I authored a detailed specification document for the data engineering team, outlining required fields, grain, and source systems for each dashboard so the upstream Snowflake models delivered exactly what reporting needed. After implementation I ran data validation and iterative refinement to ensure metrics matched source-system expectations before handoff.
Evidence: 3 → 1 — Sources unified: CRM, Web analytics, GitHub community activity into Controlled BI environment.
Responsibilities
- Designed and built three QuickSight dashboards on Snowflake-backed data for CRM, web, and community reporting, iterating through weekly client review cycles
- Redesigned the CRM and web analytics views from scratch, using existing reports only as reference for KPI selection and switching out visualizations for clearer, more concise versions
- Designed a net-new GitHub community activity dashboard end-to-end from client requirements with no prior dashboard to reference
- Authored data specification documents for the data engineering team to align upstream Snowflake models with reporting needs
- Performed data validation against source systems to confirm metric accuracy before handoff
Impact: Three data sources unified in a single controlled BI environment, giving the client's team a consolidated view of funnel, web, and community performance independent of Salesforce reporting.
Case study: https://www.stxnext.com/case-study/salesforce-optimization
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Revenue Reporting & Workflow Apps for an Energy Company — Energy & Digital Assets
Industry: energy | Role: BI & Workflow Applications Developer | Tools: Amazon QuickSight, Retool, AWS | Ongoing | End-to-end
Delivered two connected tracks — scheduled revenue reporting and interactive workflow apps — to automate manual processes and improve data quality.
The work had two connected delivery tracks: scheduled revenue reporting in Amazon QuickSight and form-driven workflow applications in Retool to replace error-prone manual processes.
In QuickSight I designed and implemented scheduled revenue reports, working from high-level requirements and mockups co-created with a product designer and the client. In Retool I built highly interactive, data-entry-oriented applications with many dynamic fields, conditional logic, and validation rules to minimize user error and ensure data quality in complex operational workflows.
Requirements were gathered and refined through daily syncs and working sessions with the client's team. These sessions also served as live demos and training — walking the client through functionality, capturing feedback, and ensuring the team could use the tools confidently in daily operations. I also produced how-to guides and documentation to support ongoing adoption.
Evidence: 2 — Delivery tracks: Scheduled revenue reporting and Retool workflow applications.
Responsibilities
- Gathered and refined reporting and workflow requirements directly from the client
- Built QuickSight dashboards for fixed-schedule revenue reporting
- Developed Retool apps with dynamic fields, conditional logic, and validation rules
- Collaborated with a product designer on high-level mockups before implementation
- Led working sessions, demos, and training so the client's team could adopt the tools
- Produced how-to guides and documentation for ongoing use
Impact: Manual processes automated. Data quality improved through validation guardrails. Client team fully onboarded.
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Rationalized BI for an Email Client Product Team — Software Product — Email Client
Industry: SaaS | Role: BI Consultant | Tools: Amazon QuickSight, Neon, Salesforce | 3 mo | End-to-end
Standardized a fragmented Salesforce reporting landscape by unifying five separate time grains in one flexible QuickSight dashboard set.
The client had accumulated many disconnected Salesforce reports — standalone charts, overlapping dashboards, and separate versions for each time grain (daily, weekly, monthly, quarterly, yearly). The goal was to rationalize this landscape and build one unified, flexible dashboard set in Amazon QuickSight.
I helped evaluate BI tools and led the recommendation toward QuickSight based on the client's Salesforce setup and long-term needs. From there I designed KPI-oriented mockups largely from scratch, using existing Salesforce reports and continuous client feedback as input, and implemented one flexible dashboard set where users could switch between daily, weekly, monthly, quarterly, and yearly views — replacing the many near-duplicate dashboards with one maintainable structure.
I also built out the full QuickSight project structure: folders, user groups, core dashboards, data sources connected to Neon, and scheduled refreshes — ensuring a smooth, company-wide rollout with up-to-date data and minimal manual work for the client's team.
Evidence: 5 → 1 — Time grains unified: Daily, Weekly, Monthly, Quarterly, Yearly into Flexible dashboard set.
Responsibilities
- Supported BI tool evaluation and recommended Amazon QuickSight
- Designed KPI-oriented mockups from scratch using Salesforce reports as input
- Built unified dashboards with flexible time-range switching (daily → yearly)
- Consolidated many overlapping dashboards into a small, maintainable set
- Designed full QuickSight structure: folders, user groups, data sources, schedules
- Configured Neon data sources with scheduled refreshes for reliable, timely data
Impact: Fragmented Salesforce reports consolidated. Company-wide QuickSight rollout delivered with full structure and governance.
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Real-Time Manufacturing Analytics for a Global Heavy Industry Manufacturer — Global Heavy Industry Manufacturer
Industry: manufacturing | Role: BI Developer | Tools: Amazon QuickSight | 3 wk | Implementation
Implemented a four-view real-time manufacturing analytics suite natively in AWS QuickSight from production design mockups, covering furnace process monitoring and a 2,000+ asset rotating equipment fleet.
The client, a global manufacturer running highly complex industrial operations, needed a real-time monitoring tool built ground-up to replace standard BI templates that struggled to prioritize critical signals across millions of raw sensor readings. The production design team partnered with subject matter experts to translate physical plant infrastructure into an intuitive digital ecosystem grounded in cognitive UI principles, and I owned the implementation of that design in AWS QuickSight.
I built the full interactive dashboard layer across four interconnected views: a Furnace Process Overview surfacing high-level situational awareness for safety calls, a Detailed Metrics view exposing point diagnostics without visual clutter, a Rotating Equipment Fleet view monitoring 2,000+ assets with intelligent status prioritization, and an Asset Deep Dive that walks operators through a clear diagnostic narrative tied to direct work order actions.
My implementation work covered visual formatting and layout, conditional formatting tied to operational thresholds, interactive filtering, cross-view drill-down navigation, and every other in-dashboard behavior end users interact with. The result was a fully native QuickSight build that preserved the design system's nuance while handling production-scale fleet data.
Evidence: 2,000+ — Assets monitored.
Responsibilities
- Built the full QuickSight dashboard suite end-to-end from production design team mockups
- Implemented conditional formatting tied to operational thresholds for at-a-glance status reads
- Wired up interactive filtering and cross-view navigation between fleet, asset, and process views
- Translated the design system's cognitive UI principles into native QuickSight components within the tool's constraints
- Iterated alongside the design team to preserve visual nuance and intent through implementation
Impact: Delivered a fully native Amazon QuickSight dashboard suite implementing the cognitive UI design system at production scale, giving operators real-time visibility into furnace process health and a 2,000+ asset rotating equipment fleet.
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Experience
- Data Analyst at STX Next (Present) — Leading dashboard and governance work end-to-end across multiple client industries.
- Jr. Data Analyst at STX Next (2024 — 2025) — Shipped client BI work in Tableau and QuickSight; supported migrations and data modeling.
- Jr. Data Analyst at Inquire Business Consulting (2023 — 2024) — Reporting and analytics for cross-industry clients; early dashboard design work.
- Software Engineer Intern at Inquire Business Consulting (2023) — Internship across data + product systems; foundation in engineering practices.
Education
- Data Science and Machine Learning — MIT Schwarzman College of Computing. Advanced modeling, experimentation, and decision-making with data.
- Python Data Structures — Universidad Austral. Foundations of Python data structures for analytics workflows.
- Bachelor's in Informatics Engineering & Digital Business — Universidad Anáhuac Mayab. Core computing, systems, and digital business foundations.
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