Hector L
The star rating is a representation of the overall rating, calculated as the mean of the client satisfaction rating, the average client interview rating, and internal interview scores.
The client satisfaction rating is the weighted average of client ratings, with weights based on reviewed work hours. When no client rating exists, the approval fraction (approved vs. reviewed work hours) determines it.
If the client satisfaction rating exceeds the overall rating, it becomes the star rating. In the absence of client ratings, if the average client interview rating is higher than the overall rating, it becomes the star rating. If no data is available, the star rating defaults to the internal interview score.
The client satisfaction rating is the weighted average of client ratings, with weights based on reviewed work hours. When no client rating exists, the approval fraction (approved vs. reviewed work hours) determines it.
If the client satisfaction rating exceeds the overall rating, it becomes the star rating. In the absence of client ratings, if the average client interview rating is higher than the overall rating, it becomes the star rating. If no data is available, the star rating defaults to the internal interview score.
Senior Data Engineer Preferred Title
$62.50 /hr $ 100.0K /yr Hourly Rate and Yearly Salary
Overview
Basic Summary
Interview Scores (3.5/5) - Software Engineering Process 4.0/5
- Design Practice 3.0/5
- Design Theory 3.0/5
- Technical Breadth 4.0/5
- Logical Thinking 4.0/5
- Technical Strength 4.0/5
- System Design 3.0/5
- Productivity and Responsiveness 3.0/5
- Teamwork 4.0/5
- Agile Development Process 3.0/5
- Intellectual Merit 4.0/5
- English Communication 4.0/5
- Documentation 3.0/5
- Desktop Mac
- Phone iPhone
- Tablet iPad
- Member Since Apr 23, 2022
- Profile Last Updated Apr 19, 2022
- Last Activity April 23, 2022, 7:38 p.m. UTC
- Location Colombia
Profile Summary
Senior Data Engineer with experience in multiple industries and performing different tasks. I have strong communication and leadership skills that I have developed by working as a consultant and leading client teams. I feel comfortable working with different DE tools and frameworks. I have expertise with Azure Cloud (Data Factory, Databricks, SQL Server, Analysis Services, Blob Storage, Power BI) and Google Cloud (Cloud Storage, BigQuery, Pub/Sub, Cloud SQL, Cloud Spanner, Composer
Skills
Total Experience: 6+ years
SQL (3E, 5Y)
3 experiences, across 5 yearsGoogle Cloud (3Y, 1C)
3 years of experience, with 1 courseAzure (2E, 4Y)
2 experiences, across 4 yearsPower BI (3Y)
3 years of experiencePostgres (3Y)
3 years of experienceOracleDB (3Y)
3 years of experiencePython (3E, 2Y)
3 experiences, across 2 yearsApache Spark (3E, 2Y)
3 experiences, across 2 yearsETL Extract Transform Load
Weekly Availability
Timezone Overlap with 06 - 21 per Week: 29h PST, 26h UTC
Day
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
UTC
15 - 24
20 - 24
20 - 24
20 - 24
20 - 24
20 - 24
00 - 24
PST
08 - 17
13 - 17
13 - 17
13 - 17
13 - 17
13 - 17
17 - 17
Vetting
- Interview Data
- Software Engineering Process
- Design Practice
- Design Theory
- Technical Breadth
- Logical Thinking
- Technical Strength
- System Design
- Productivity and Responsiveness
- Teamwork
- Agile Development Process
- Intellectual Merit
- English Communication
- Documentation
- Code Quality
- Code Readability
Experience
Senior Data Engineer
Employment
Jan 2022 - Present
Google Company
Technology Industry
Project: GCP consulting
- I provided the client advice on designing the right architecture for their needs within Google Cloud.
- I helped them to configure Pub/Sub notifications each time a new event occurs within Google Cloud Storage (GCS) and do some logic based on these events.
- I provided insights on how to define the Google Cloud Spanner schema and best practices for ingestion, configuration, and deployment.
Senior Data Engineer
Employment
May 2021 - Sep 2022
McKinsey Company
Energy Industry
Project: BI project for oil company
- I worked on this project as a backend engineer and data engineer. We used Azure Data Factory as orchestrator, Azure Blob Storage, and a SQL database to store the data, the front-end was a web app.
- I used Python + Apache Spark to automate data quality validations on multiple spreadsheets and return feedback. Based on the feedback some reports were rejected (by the app) and asked for corrections.
- Once all the reports were received and accepted, the data was processed and stored in a SQL database. From there, I created the logic to interact with the web application.
- The web application receives multiple parameters and based on those I created queries and stored procedures to retrieve the information to calculate multiple KPIs and display charts.
Senior Data Engineer
Employment
Jan 2021 - Apr 2022
McKinsey Company
Food and Agriculture Industry
Project: Commodity pricing
- I used Apache Spark + Databricks to cleanse and prepare data for a DS model that predicts the price of a commodity.
- Using Python I created a connector to interact with the Bloomberg API and get commodity prices.
Senior Data Engineer
Employment
Sep 2021 - Dec 2021
McKinsey Company
Finance Industry
Project: Propensity model for car insurance
- As a McKinsey consultant, I led a team of four data engineers to create this product. I designed the architecture and assigned tasks based on my expertise.
- I created ETL Extract Transform Load pipelines to move data from on-prem data sources to Azure Cloud using Azure Data Factory. The data was processed using Apache Spark + Python + Databricks.
- The data was stored in a SQL databased and consumed by an ML model.
Senior Data Engineer
Employment
Dec 2018 - Oct 2020
Gases del Caribe Company
Energy Industry
Project: BI Department
- I was part of the team that created an entire BI department in a non-tech company. The company was a mid-sized utility company without any previous analytical experience.
- I was responsible to design the ingestion architecture and the data marts. The main source of data was an OracleDB, and we used a Postgres replica as an intermediate step.
- The data was moved into the Azure Cloud using Azure Data Factory. After this, the data was cleansed using mainly SQL Stored Procedures. We used Azure DevOps repositories.
- Finally, I was responsible to create multiple dashboards for different areas of the company. I did this by using Power BI and DAX.