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@matttanous
Senior Data & Analytics Engineer building scalable cloud and AI-driven pipelines.
I am a Senior Data & Analytics Engineer with 13+ years designing scalable data infrastructure, ELT pipelines, and analytics platforms across logistics, retail, SaaS, and real estate. I combine deep technical expertise across AWS, GCP, Azure, and OCI with business insight to deliver data solutions that accelerate decision-making and operational growth.
I have led projects that reduced manual reporting by 60%, improved onboarding throughput by 25%, and cut documentation effort by 40% using AI-assisted workflows. My experience includes building event-driven ingestion, CI/CD for data workflows, data lineage and governance, and production ML pipelines using tools like dbt, Airflow, Spark, Kafka, Snowflake, BigQuery, and scikit-learn.
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Work history, roles, and key accomplishments
Doorvest
Jul 2023 - Sep 2025 (2 years 2 months)
Designed a unified metrics layer with dbt and DuckDB and built ELT pipelines into BigQuery, reducing manual reporting by 60% and increasing onboarding throughput by 25% through automated verification and AI-assisted documentation.
Built usage metering and event-modeling pipelines with dbt and Snowflake to support per-seat billing and retention analytics, and automated metadata ingestion across 300+ datasets to improve discoverability.
The Home Depot
Apr 2015 - Aug 2018 (3 years 4 months)
Delivered product and store performance Tableau dashboards and automated SKU refreshes, cutting manual effort by 70% and consolidating legacy workflows to reduce report duplication by 40%.
FedEx
Jun 2012 - Mar 2015 (2 years 9 months)
Contributed to data warehousing for the SenseAware IoT platform and built Tableau dashboards and SQL Server procedures to monitor SLAs and visualize delivery patterns, improving route efficiency through modeling.
Degrees, certifications, and relevant coursework
Computer Engineering
2007 - 2012
Completed a degree in Computer Engineering with coursework covering hardware and software foundations, embedded systems, and computer architecture.
Software and tools used professionally
Rudderstack
Amazon Redshift
Snowflake
Airbyte
Fivetran
Azure Synapse
Apache Spark
AWS Glue
Apache Flink
Tableau
Looker
Superset
SAS
Dataform
Amazon EC2
Microsoft Azure
Amazon CloudWatch
Amazon S3
AWS Step Functions
GitHub
GitLab
Kubernetes
Amazon EC2 Container Service
Amazon EKS
Azure Kubernetes Service
Amazon CloudFront
Jenkins
CircleCI
GitHub Actions
GitLab CI
Salesforce
Jupyter
NumPy
Pandas
Data Miner
dbt
DB
MySQL
PostgreSQL
Microsoft SQL Server
MariaDB
Oracle
Gmail
Node.js
Google Analytics
Databricks
Terraform
AWS CloudFormation
Visual Studio
PyCharm
Azure DevOps
React
JavaScript
Python
Java
CSS 3
C#
Scala
R Language
AWS Elastic Load Balancing ...
Amazon Machine Learning
Azure Machine Learning
TensorFlow
PyTorch
MLflow
scikit-learn
Keras
Kafka
Amazon SQS
Grafana
Prometheus
SQLAlchemy
Confluence
GraphQL
Google Cloud Dataflow
gRPC
AWS Lambda
Amazon RDS
Amazon Aurora
Azure SQL Database
TypeScript
pytest
Visual Studio Code
RStudio
GitHub CLI
Amazon Cognito
Docker
Airflow
Azure App Service plan
Heatmaps.io
Google BigQuery
AWS Elastic Beanstalk
Amazon Web Services (AWS)
Microsoft Power BI
SQL
Amazon SageMaker
Google Cloud Dataproc
DuckDB
Hightouch
Hex
DataHub
OpenAI API
Great Expectations
ArgoCD
Bash
Alloy
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