Analytics Engineer · Data Engineer · Seattle, WA

Nahom Debela

I build reliable data pipelines, dimensional models, and analytics products on Azure — turning complex source data into decision-ready systems used by business teams.

4+ years across SQL, Python, Power BI, Azure Data Factory, Azure SQL, and Databricks, supporting enterprise analytics including Microsoft's Azure Commercial business.

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Portrait of Nahom Debela
0% reduction in pipeline maintenance costs
0+ workflows migrated with zero downtime
0+ users reached by tools and analytics I delivered
0+ hrs/week manual reporting and data-entry work eliminated through automation

Evidence before elegance.

I'm an analytics engineer with 4+ years working across the point where data engineering meets analytics: turning messy source data into reliable models, pipelines, and reporting that people can actually make decisions from.

Most recently, I helped build a wildfire-risk analytics solution for Consumers Energy, combining wildfire, weather, and geospatial data into a Power BI dashboard used to support multi-million-dollar mitigation planning. The work went beyond visualization — it included Python-based data preparation, data-quality validation, dimensional modeling, performance optimization, and solving geospatial constraints that required rethinking how the data could be represented.

Before that, I spent four years at TCS supporting Microsoft's Azure Commercial business. I built SQL-backed analytics and business applications, developed Azure Data Factory pipelines, automated recurring reporting and operational workflows, and helped clean up the systems behind them. One of those efforts eliminated 25+ redundant pipelines and reduced maintenance costs by 30%; another migrated 40+ workflows to Azure Logic Apps with zero downtime.

Toolkit

The mechanics behind the work.

Querying & Languages

  • SQL
  • Python (Pandas, NumPy)
  • DAX
  • KQL
  • M (Power Query)

Visualization & Reporting

  • Power BI (data modeling, DAX, star schema)
  • Advanced Excel (XLOOKUP, pivot tables)
  • Dashboard Development
  • KPI Reporting

Data Engineering

  • Azure Data Factory (ETL/ELT, incremental loading)
  • Databricks
  • Azure SQL Database, ADLS Gen2
  • AWS S3

Delivery & Governance

  • Stakeholder Management & UAT
  • Requirements Gathering
  • Technical Documentation
  • Agile/Scrum

Work History

Two roles, one throughline: make the data trustworthy, then make it usable.

Nov 2025 – May 2026

Senior Data Analyst

Logic 2020 — Consumers Energy | Seattle, WA

  • Designed and built a Power BI wildfire-risk dashboard and underlying data model, integrating a 400K+ record, 300+ column dataset to support multi-million-dollar mitigation planning decisions for Consumers Energy.
  • Used Python and pandas to ingest data from more than five sources — including wildfire, weather, and geospatial datasets — into AWS S3, performing EDA, transformation, and data-quality validation.
  • Led UAT walkthroughs with utility stakeholders, captured feedback, and iterated on the dashboard and data model, resulting in higher stakeholder satisfaction and fewer revision cycles.
  • Optimized the Power BI data model, relationships, DAX measures, and filters for high-cardinality geospatial data, improving dashboard responsiveness and performance.
  • Evaluated ArcGIS and Azure Maps when the original ~200K-polygon approach exceeded Power BI's mapping limits, then shifted to point-based visualization while preserving the client's ability to use the polygon file in their own GIS software.
Via Logic 2020

Aug 2021 – Nov 2025

Analytics Engineer

Tata Consultancy Services — Microsoft | Bellevue, WA

  • Built and maintained 10+ SQL Server tables, views, and stored procedures supporting an Azure Marketplace business application and downstream Power BI reporting across a $2B+ Azure Commercial business.
  • Developed parameterized Azure Data Factory pipelines to ingest and transform data into Azure SQL, incorporating scheduling, validation, and error-handling logic for reliable downstream analytics.
  • Reduced pipeline maintenance costs by 30% by auditing and deprecating 25+ data pipelines across production environments, and led the migration of 40+ workflows to Azure Logic Apps with zero downtime.
  • Partnered with cross-functional stakeholders to translate business requirements into SQL-backed applications and Power BI solutions, leading UAT, documentation, and post-launch support for tools used by 50+ stakeholders.
  • Replaced two manually-produced daily reports with automated Power BI + Power Automate solutions using DAX, KQL, and Power Query, improving timeliness and consistency of stakeholder reporting.
Via Tata Consultancy Services

Education & Certifications

B.S. in Data Analytics, Minor in Business Administration

Washington State University · 2019–2021

Databricks Data Engineer Associate

Certified · September 2025

Microsoft Certified: Fabric Data Engineer Associate

Expected · October 2026

Selected Work

Real business problems, built end to end.

Power BI Python Geospatial Analytics

Power BI Wildfire-Risk Intelligence Dashboard

A wildfire-risk dashboard for Consumers Energy's service territory, combining internal asset and geospatial data with public weather, FEMA, and wildfire datasets. Data science produced ignition-risk scores from a model I helped feed with validated source data; I owned the downstream transformation, Power BI data model, and delivery.

Techniques Used

  • Python (pandas) transformation of a 400K-record, 300+ column zone-level dataset for Power BI consumption
  • Derived reporting fields including ignition-risk-score buckets, split into zone- and feeder-level datasets
  • Power BI data model and DAX measures optimized for high-cardinality geospatial fields
  • Evaluated ArcGIS and Azure Maps after the original ~200K-polygon approach exceeded Power BI's mapping limits

Key Finding

The original requirement — visualizing ~200K zone polygons directly in Power BI — exceeded the platform's rendering limits. Shifting to point-based visualization (while handing the client the full polygon file for their own GIS software) preserved reporting speed without losing the underlying geospatial precision they needed elsewhere.

View on GitHub
Azure Data Factory Databricks Personal Project

End-to-End Spotify Data Engineering on Azure

A production-style Azure data engineering project using ADF, ADLS Gen2, Databricks, Delta Lake, Unity Catalog, and Lakeflow to build metadata-driven incremental ingestion and dimensional Gold models for Spotify-themed data — designed using production-style patterns including parameterization, incremental loading, metadata-driven orchestration, checkpointing, and governed Delta tables.

Techniques Used

  • Metadata-driven ADF ingestion across five entities using parameterized ForEach pipelines
  • Incremental Azure SQL → ADLS Bronze loads using CDC watermarks and controlled backfills
  • PySpark and Auto Loader transformations into Silver Delta tables with streaming checkpoints
  • Unity Catalog governance using managed identities, storage credentials, and external locations
  • Gold-layer dimensional modeling with Lakeflow/DLT, Auto CDC, and SCD Type 2
  • Jinja-based metadata-driven SQL and Databricks Asset Bundles for reusable deployment configuration
Azure Data Factory pipeline diagram: Lookup last_cdc and Set variable current feed into Copy data AzureSQLToLake, which feeds an If Condition IfIncrementalData branching to max_cdc/update_last_cdc on true or DeleteEmptyFile on false
Pipeline diagram — incremental load with CDC watermark branching (Azure Data Factory)

Key Finding

Built a reusable end-to-end pipeline from Azure SQL → ADF → ADLS → Databricks → Delta/Lakeflow, with metadata-driven ingestion, incremental processing, governance, and SCD2 history.

Power Platform SQL ML Integration

Azure Marketplace Business Application

Microsoft's Azure Commercial Marketplace team was maintaining offer categories manually in Excel — products carried 1–5 loosely-defined keywords instead of one primary category, which quietly degraded reporting accuracy. Built a self-service application to fix it.

Techniques Used

  • Power Platform application with SQL Server backend for storage, querying, and transformation
  • Lookup tool with insert/update/delete, access restricted via managed security group, deep-linked to Power BI
  • Integrated a machine-learning-generated primary-category field once the data science team delivered it
  • UAT, approval workflow, email automation, usage documentation, and a recorded walkthrough for adoption

Key Finding

The application and its downstream Power BI reporting reached 50+ stakeholders, and the ML-generated category eliminated the manual entry that had been degrading reporting accuracy — turning a maintenance chore into a governed, self-service system.

Power BI Power Automate KPI Reporting

Automated Reporting Process — Microsoft 365 Marketing

Two reports were being rebuilt by hand every day: download from ICM and Power BI, transform in Excel, recreate the report. Slow, error-prone, and often stale by the time stakeholders saw it.

Techniques Used

  • DAX and KQL queries against Azure Analysis Services and Kusto, validated against the prior manual output
  • Power Automate to generate and email Excel reports on a daily schedule, replacing the manual rebuild
  • Two Power BI reports (DirectQuery + Import Mode) covering CTR, ROI, and campaign performance, with incremental refresh configured in the Power BI service

Key Finding

Beyond automating the rebuild, the new reporting surfaced a signal the manual process never had time to check: campaigns with high impressions but low click-through — a direct flag for creative that needed refreshing.

Power Automate Azure Logic Apps Platform Migration

Power Platform → Azure Logic Apps Migration

A Power Platform environment was being deprecated after a reorg, putting dozens of business-critical workflows at risk — and some Power Automate actions had no direct Logic Apps equivalent, so the automation logic couldn't just be copied over.

Techniques Used

  • Evaluated three migration paths (new Power Platform environment, Azure Logic Apps, or rebuild in code) and presented the trade-offs to stakeholders
  • Manually recreated unsupported workflow logic for ~40 flows after Logic Apps was chosen
  • Tested each migrated flow individually and tracked status with daily updates through sprint meetings

Key Finding

Completed ahead of the deprecation deadline with zero downtime and no service interruptions — the riskiest part of the project (unsupported actions with no direct equivalent) was solved case-by-case rather than assumed away.

Azure Data Factory Data Governance Cost Reduction

ADF Production Pipeline Deprecation & Cleanup

An Azure Data Factory environment at Microsoft had accumulated unused and failing pipelines over years — quiet cost and maintenance drag with no clear owner for cleanup.

Techniques Used

  • Compiled a full pipeline inventory, traced each one to its owning team, and reviewed run history
  • Gave owners a one-month notice window to confirm whether a pipeline was still required before removal
  • Validated safe removal with stakeholders, then deprecated 25+ unused or failing pipelines across three production environments

Key Finding

Reduced pipeline maintenance costs by 30% — proof that a systematic audit-and-notify process can safely remove technical debt in a live production environment without breaking anything, and without just deleting first and asking later.

On My Own Time

What I build when nobody's asking.

Automation Personal Finance

Personal Investing Dashboard & Trading Agent

A self-built system for tracking and researching my own investment portfolio — built because I wanted the same rigor I bring to enterprise dashboards applied to my own decision-making, not because I trust a black box to trade for me.

What It Does

  • Integrates with a brokerage API to pull live position and market data
  • Database-backed watchlist and trade log for structured, reviewable research notes
  • An "AI Analyst" feature that summarizes market data and surfaces research questions
  • Scheduled automation that checks positions and watchlist criteria on a recurring basis, with all trade decisions reviewed by me before execution
Nahom Debela hiking with a mountain view in the background

Outside of dashboards — Pacific Northwest hiking.

Get In Touch

Let's talk about your data.

Open to Analytics Engineering, Data Engineer, and BI Engineer roles — Greater Seattle area or remote.

[email protected] linkedin.com/in/debela github.com/nahomdebela0 Download Resume (PDF)