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Senior Data Engineer · Analytics Engineering · Fraud & Revenue Data Platforms

Alex Huggler

I build production-grade data platforms that reduce fraud loss, improve revenue visibility, and turn ambiguous business problems into reliable analytics systems.

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Selected work

Featured projects

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Project 01

repo

Real-Time Fraud Signals Pipeline

Simulates fraud-event detection across streaming telecom-style data, with rules + statistical anomalies surfaced in seconds.

Kafka → Spark Structured Streaming → Delta → dbt → Streamlit, with watermarked windows, exactly-once writes, and dbt tests on the marts.

  • Apache Kafka
  • Spark Structured Streaming
  • Delta Lake
  • dbt
  • Streamlit

What this demonstrates

  • Streaming ingestion + watermarks
  • Data quality / dbt tests
  • Fraud analytics architecture
  • Production-style local dev
View interactive demo

Project 02

repo

Telecom Billing Lakehouse

Builds a single source of truth for revenue, ARPU, and churn signals over high-volume Call Detail Record data.

Airflow → MinIO/S3 → Iceberg Bronze/Silver/Gold → Great Expectations contracts → dbt marts, with Terraform for the AWS variant.

  • Apache Airflow
  • Apache Iceberg
  • MinIO / S3
  • Great Expectations
  • dbt

What this demonstrates

  • Medallion lakehouse design
  • Data contracts at the boundary
  • dbt modeling for BI
  • Orchestration + IaC
View interactive demo

Project 03

repo

AI-Assisted SQL Optimizer

Closes the gap between “a query that runs” and “a query a senior would ship” for Spark SQL and Snowflake workloads.

sqlglot AST + heuristic findings feed a structured Claude prompt; optional EXPLAIN runner scores rewrites against a 50-query corpus.

  • Python
  • Typer
  • Anthropic SDK
  • sqlglot
  • pytest

What this demonstrates

  • Applied LLM tooling
  • SQL parsing + heuristics
  • Benchmark methodology
  • Engineer-in-the-loop UX
View interactive demo

Prefer charts to code? Explore the interactive data visualizations built from these same pipelines.

About

Senior data & analytics engineer

I'm a senior data and analytics engineer with six years at AT&T building production data platforms on Azure and Databricks. My work spans PySpark ingestion, dbt analytics models, BI delivery, streaming systems, and data quality. I specialize in turning ambiguous fraud, revenue, and operational problems into reliable pipelines and decision-ready analytics.

Recent impact includes identifying or enabling $900K+/yr in fraud-loss reduction and supporting analytics for a $2.2B portfolio of revenue and operational data — work that spans streaming detection, lakehouse design, and BI delivery on AT&T's data platform.

I treat tests, observability, runbooks, and data contracts as part of the deliverable, not as follow-ups. The projects above demonstrate that approach end-to-end: streaming pipelines with watermarks and exactly-once semantics, lakehouses with data quality at the boundary, and AI tooling with corpus-tested behavior.

Certified DP-203 Azure Data Engineering DP-100 Azure AI Engineering

Current focus

Stack

Let's talk

Interested in data platforms, analytics engineering, or BI automation?

I'm open to senior data engineering, analytics engineering, and data platform consulting conversations.

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