I love to teach. I post about #dataengineering and #mentalhealth daily. 

Many years of data engineering experience doing growth analytics at Facebook, security infrastructure at Netflix, and pricing and availability infrastructure at Airbnb. 

You can find me by looking up EcZachly on all social networks 

Join my Data Learning Platform https://www.dataexpert.io

Zach Wilson

Databricks is going to win “data” the same way that Meta won “social media” 

They are uniquely positioned because of the convergence of a few trends:

- the death of the data analyst
We are moving away from analytical architectures and towards action-oriented architectures. Instead of “the data engineer does ETL so analysts can look at it” it will be “the data engineer creates context for AI agents to do something” Actions will reduce timelines and speed up business velocity. Humans will no longer be the analytical bottleneck. 

- the crushing of data siloes 
Open table formats like delta and iceberg will make business able to do things that were previously though impossible! We’ll finally have all the context for our AI in one place. Entire business context + Agents = a waterfall of business value! 


I’m launching a paid Databricks boot camp on February 9th. Send me a DM with the word Databricks and I’ll get you the details!

Founder @ DataExpert.io | ADHD | 500k+ followers | Angel Investor

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The first speaker lineup for the analytics engineering boot camp is going to be so amazing! 

These sessions are what truly separates my boot camp from “just another data course”

Data modeling is far from dead! It’s actually more relevant than ever. 

There’s been an interesting shift in the seas with AI. Some people saying we don’t need to do facts and dimensions anymore. This is a wild take because product analytics don’t suddenly disappear because LLM has arrived.

It seems like to me that multi-modal LLM is bringing together the three types of data:

- structured

- semi-structured

- unstructured

Dimensional modeling is still very relevant but will need to be augmented to include semi-structured outputs from the parsing of text and image data.

The necessity for complex types like VARIANT and STRUCT seems to be rising. Which is increasing the need for data modeling not decreasing it.

It feels like some company leaders now believe you can just point an LLM at a Kafka queue and have a perfect data warehouse which is still SO far from the actual reality of where data engineering sits today

Am I missing something or is the hype train just really loud right now?

I'm doing a live with Karan Dwivedi to talk about a cybersecurity boot camp that is launching on January 9th! 

This boot camp has an amazing capstone project where you do all of these on a company server!
- threat modeling
- detection engineering
- preventative controls 

Don't miss this webinar tomorrow!

January 9th Cybersecurity Boot Camp Webinar

Consumer Financial Protection Bureau

Supporting the agency with modernization efforts and technology implementation. Implementation of a new technology that added new features for release in 2021. A joint effort with Consumer Response and Technology & Innovation. Daily use of postgres, docker, SQL, Python, AWS(EMR,S3,EC2), and Elasticsearch.

2021 projects:
- Provide support to Complaint Analytics.
- Developing docker environments and settings for testing pipelines.
- AWS development POC (stream and batch processing) using EMR and open source D.Q.
- Provide support to cross functional teams.
- Create ETL process to manage metadata for all data pipelines used and create data model.
- Experience with AWS EMR, Kinesis, API Gateway, S3, Glue, cloudwatch, and lambda.
- Building docker containers using YAML and images used PGADMIN, Postgres, Elasticsearch, and Kibana.


Successful outcomes 2021:
• Provided product's continued success in development by processing 7.5 million records in search.
• Implemented concurrency process for pipeline completion and increase efficiency by 50%.
• Reduced OCR data processing time by 50%. More than 5,000  data points processed daily.
• Added new features to display statistics for data pipeline processing.
• Modernized tech stack by refactoring data pipelines to improve performance and leverage new technologies.
• Implemented Python data processing from database to Elasticsearch api for CFPB internal tool (400,000+ data points processed in July). 
• Worked with developer to provide business requirements to correct 200,000+ data points affecting review of process.


-Created workflows and roadmap for organization to build many data sources from data
lakes to data bricks environment.

Data Engineer

Accenture Federal Services

Discovery Lab Product Development:

Projects 

- Building a data collection automation process for management.
-  Use of Natural Language Processing to extract and analyze text data.
-  Used Spacy package to extract text data from sources for a Machine Learning model.
-  Work with Program Manager to test use cases and scenarios for the product.
-  Develop automation process using Python and SQL tools.
-  Work with team members in project to polish and optimize pipeline code.
-  Develop data pipelines using python from different data collection sources.
-  Web scrape and develop data collection scripts for automation using Beautful Soup and Selenium.


Operations Lead:

-  Promoted to lead AWS project efforts and managing two Data Engineers. 
-  Build Data Pipeline using python scripts
- Work with team to set up APIs for scalability (Amazon API Gateway)
- Lead the Engineering Group
- Attend Data Science modeling group for data support
- Provide updates to leadership and sales
- Work with Data Governance lead to build database standard

Data Scientist | Applied Intelligence

Applied Intelligence 

Powered by Management Consulting and Technology, AFS Applied Intelligence (AIX) is a full-service data-native practice group that helps federal departments and agencies unlock business value from their data, improve visibility and performance across the enterprise, enhance decision-support and mission outcomes, and accelerate digital capabilities at scale. 

We are at the forefront of helping federal agencies apply more innovative approaches to achieve their mission objectives and improve operations. As leaders in advanced analytics, artificial intelligence (AI), and automation, our cross-disciplinary team of data science, data engineering and data visualization strategists and practitioners provide deep technical, functional and mission expertise, innovative solutions, and tested methodologies that help agencies collect and measure what matters to them, intelligently harness emerging technologies, optimize decision-making, and holistically and responsibly drive end-to-end change.

Capabilities

Our offerings for the Federal sector are designed to support customers at every phase of their data journey:

Artificial Intelligence and Automation
We help our clients establish strong practices and executable roadmaps that set the stage for any AI or intelligent automation transformation. Our offering includes AI Strategy, AI Systems and Service Design, Responsible AI, Conversational AI, and Robotic Process Automation.


ASSETS AND ACCELERATORS

We support portfolios and our AFS clients through multiple channels: 
The Discovery Lab offers prototyping, strategy, and design services across the full spectrum of Applied Intelligence offerings. Co-located at our Digital Studio in Washington, D.C., the Discovery Lab is our hub for data discovery, innovation, and emerging technologies.

Operations Lead (Remote) | Applied Intelligence

Verisk

Earned Verisk Way to Go Award for excellence in data analysis and research:

issue of misreporting 1,300 policies for Geico from a data load was resolved through data analysis and collaboration with development to resolve any issues to state DMV.


Product Owner: 

Guidewire Accelerator. Released Product that supports client's data implementation process.

Coverage Verifier Conversion Project. Released first iteration of program to clean up data preprocessing step. 

Data Quality Team :
Analyzed current format of data ingestion process. Communicate with management on process to improve data processing.  Capture missing data elements for each customer in QA process. Train team members to report data quality process, business logic, and implementation of new logic for customers.  Work with development team and QA team to test data program and business logic.  Prioritize and release features for the project in two week sprints in agile.

Migration Consultant:
Worked with ETL group to consult business logic and QA for Coverage Verifier migration from Legacy (Mainframe) to the cloud (AWS). Worked extensively with group during meetings to provide business requirements.

Technology Solutions:
Worked extensively to study Coverage Verifier flow to understand issue of missing reports from the clients. Investigate files transfer protocol from the Mainframe to production SFTP server. Discovered with team of the corrupted files when the network disconnects. Documented and reported incident to management to execute a solution with data team and data quality team.

Business Analyst

WeLIFT.co

As a Data Engineer, my role is to migrate processes from the old system to a new platform. Before the analysis, I need to build the structure and pipes. Once this process is completed, I will be able to  generate reports and collect and manage data flow for the organization.

Lead Data Scientist

MedAssets

Client: RWJ Barnabas Health

Identified data quality issue within client's application and used SQL functions to update  database  records. Leveraged the test environment to quickly test and create a new table and data set to push the update to production. I quickly resolved the issue for Robert Wood Johnson's one of many facilities in New Jersey experiencing inefficiencies in data quality.

Data Analyst

New Jersey Citizen Action

Data Analyst:
Provided industry and technical knowledge on customer relationship management for a new start up. Provided QA for new data collection system.

Jersey City project:
Manage the organization's project to provide financial services to communities in Jersey City. Consult on best practice on financial services and products for consumers who are in debt. Collect data during interviews and provide an outline to clients looking to improve their finances and decrease spend.

Consultant

Ipsos

Mastercard 2015 Project:
 Analyzed, cleaned, and standardized data for Mastercard's credit card data for the North American Market. Worked with Ipsos Loyalty director, Mastercard consultant, and intern to prepare the data for analysis by using outlier detection method, statistical inference and comparison of means to understand consumers's spending habits and top ten credit card usage in their wallet. The success of Mastercard's consumer and small business study in 2015 resulted in expansion of the study in 2016 to cover South American and Canadian markets. 

My Personal Accomplishment:
Teaching my intern on the use of SPSS syntax to automate data cleaning process, and working together to prepare the data for our client.

Statistical Analyst

Kids Corporation II

•	Administered surveys to one hundred students for summer program evaluation
•	Created surveys to collect data on the impact of afterschool programs in Newark Public Schools
•	Used Microsoft Excel and IBM SPSS tools to create charts and tables for Statistical Analysis
•	Created a final report for partners to continue funding Kids Corporation II for Summer 2015

Research Associate

The Financial Clinic

	Managed two sites in New Jersey for direct services and achieved contract obligation
	Documented and tracked customer data for data manager
	Provided workshops and presentations for NY/NJ customers 
	Formed partnerships with non-profit organizations throughout New Jersey
	Invited speaker for the Lift Up project in Savannah, Georgia for services and issues that come with financial hardships

Financial Fellow

Passionate and energetic data professional who loves a challenge. I am interested in intellectual work with rigour. I look for opportunities that offers unique perspectives on social and economic issues. I consult for companies in delivering results for their business using theoretical and mathematical concepts.

I have implemented technology solutions for government agencies, healthcare hospitals, financial services, and Insurance companies (16 clients). Able to efficiently solve the customer's ETL project issues. Work with technical and non technical staff to provide clarity and solutions to customer's needs in technology. 

I enjoy working with people and helping them achieve their potential to reach success. I build partnerships and relationships with my clients, and work with them to meet their needs. I work with different businesses and IT development teams to work on technical and operational improvements that lead to innovation.

Indoor and beach volleyball player when not doing data science in my free time.

Specialties:
•Data Science (Natural Language Processing, Regular Expression text extraction)
•Data Engineering (AWS cloud implementation)
•Product Management
•Writer Contributor (Guidewire manual)
• Python Developer
• Customer Implementation
• Configuration Management and Cloud Lead

Program Languages/Technologies:
•R
•Python (Pandas, Spacy, Great Expectations, Boto3)
•Pyspark
•Docker
•SQL (Mysql, Microsoft SQL Server Management Studio, DB2, TablePlus, Postgres)
• Cobol/JCL
• Mainframe
•  Linux (Ubuntu)
• Go Lang
• Stanford Core NLP
• AWS (S3, RDS, Glue, API Gateway, Lambda function)
• Azure Devops (Data Pipeline, Git)
• Tableau (Stacked Bar Chart, Heat map, Dual Axis Graph, Custom Calculations)
• Tesseract OCR  & Google Cloud Vision API 
• Salesforce (SOQL Query and REST API)
• Puppet and Ansible

Accomplishments:
2020 CFPB: Technology implementation & Concurrency

2020 Project Covid-19: Worked with Data Governance lead to build ETL solutions for government agencies and develop the product with the engineering team. Developed scripts alongside engineering team for automation process.

2018 Guidewire Accelerator:  Working with Development and Business Analysts in testing and analyzing data for this new product to help insurers convert data easily from their system.

2015 Mastercard Consumer and Small Business Study: The study resulted in expansion of the work in 2016 to cover South American and Canadian markets. Understanding credit card usage from consumers and small business owners.

For speaking and podcasts opportunities, reach out here.

Henry Orlando Clavo

🎙️ Next Guest Announcement

Excited to welcome Ivonne Kinser  as the next guest on the Orlando Insights Podcast.

Ivonne brings a thoughtful perspective on leadership, communication, and professional growth, and I’m looking forward to a great conversation.

📅 January 16, 2026

#OrlandoInsights #Podcast #Leadership #ProfessionalGrowth

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