Henrique Ribeiro
Senior Data Engineer
Lisbon, Lisbon, Brazil
Summary
👤
Senior🎓
Top SchoolHenrique Ribeiro is a Senior Data Engineer with 13 years of experience and over 5 years focused specifically on data engineering, building scalable ETL and real-time pipelines across Azure, GCP and AWS. He is Databricks Certified and routinely architects Medallion-style Bronze/Silver/Gold solutions using PySpark, Delta Lake and Synapse to turn raw JSON and streaming data into analytics-ready datasets. Henrique has a proven track record optimizing Spark jobs and cluster configurations to reduce runtimes and costs, and has automated generation of external tables and data catalogs from complex model JSON structures. He contributed backend improvements to the high-profile Dask project, enhancing DataFrame/Array functionality which reflects his comfort with parallel computing and library-level engineering. Based in Lisbon (Brazil) and open to remote/international roles, he pairs a postgraduate Data Science background with hands-on delivery for clients like Nestlé, BAT and Nestlé, driving data quality, governance and real-time insights. Colleagues know him for modular, reusable frameworks and a pragmatic focus on validation and observability that keeps pipelines reliable in production.
13 years of coding experience
4 years of employment as a software developer
Postgraduate Degree, Data Science, Postgraduate Degree, Data Science at DescomplicaEstácio de Sá University
Github Skills (15)
dataframes10
pandas10
dask10
python10
dataframe10
numpy10
parallel-arrays9
typed-array9
multidimensional-arrays9
pytest9
testing9
dynamic-array9
arrayobject9
scikit8
scikit-learn8
Programming languages (10)
Github contributions (5)
dask/dask
Feb 2018 - Jul 2020
Parallel computing with task scheduling
Role in this project:
Back-end Developer
Back-end DeveloperContributions:11 PRs, 59 comments, 6 issues in 2 years 5 months
Contributions summary:Henrique contributed significantly to the Dask library, focusing on enhancing its DataFrame and Array functionalities. Their work includes adding new methods like `to_series`, `to_frame`, and `divmod`, and integrating features like the `min_count` parameter for `groupby.sum`. They also addressed issues related to data type and name propagation when creating Dask DataFrames from arrays and improved the drop function.
pythonschedulingparallelnumpydask
henriqueribeiro/aws-genomics-workflows
Jan 2021 - Aug 2022
Genomics Workflows on AWS
Contributions:6 releases, 2 reviews, 23 pushes in 1 year 7 months
pipelinegenomicsawsworkflowsworkflow
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