About Me
Hey! I’m Fu and I have about 7 years of experience working with data, more recently focusing in analytics engineering. I find enjoyment in interpreting and wrangling data into assets that facilitate access to reliable, actionable information!
I started my career as an Excel guy, began constructing dashboards with various data viz tools, and then moved on to building ETL pipelines and delivering data models. With this baggage, I’ve been shifting towards learning SWE skills and best practices to keep up and become a better fit for the rapidly evolving fields of analytics and data engineering.
I’m pretty confident with: SQL, dbt, BigQuery, Power BI, git
I’m fairly comfortable with: Airbyte, Prefect, Airflow, Tableau, Python
I know the basics of: Spark, Scala, Docker
Experience
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Collaborated in Stripe migration project by rebuilding over 20 dbt models to consolidate legacy and new data in BigQuery, delivering crucial revenue related metrics;
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Reorganized scheduling dependencies of ELT pipeline (Airbyte + Prefect), reducing lead time by 40% to address problems with stale data;
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Implemented extensive dbt testing suite for MRR data models, building trust with stakeholders by alerting and addressing anomalies caused by corner cases and upstream quality issues;
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Supported business teams with self-service Looker dashboards and Hex notebooks, addressing recurring ad-hoc analytics needs.
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Supported business analysts in mapping/building datasets with Spark and Scala, ensuring proper documentation and unit testing;
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Mapped existing ecosystem of collections datasets and its dependencies, leading to the deprecation of over 30 redundant/inconsistent datasets, reducing costs and load on pipeline;
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Worked on delivery of core datasets containing source of truth accounting data used in various reconciliation, collections and finance processes. Learned about Spark UI and performance optimization to reduce lead time and ensure data freshness;
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Deployed multiple bespoke SQL tests to monitor and warn users of potential upstream anomalies or data quality issues in key datasets.
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Worked in the migration of over 20 dashboards from Tableau and Quicksight to Power BI, revamping data modeling / business metrics, and ensuring a smoother transition between tools;
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Continuously supported the data analyst chapter with performance tuning in SQL queries, resulting in direct savings in AWS Athena;
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Supported development of datasets by migrating business users’ SQL queries into Spark Scala code;
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Created a Power BI style guide, with best practices in development & data visualization and publishing a standardized report theme (color palette, company branding, fonts).
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Developed ETL pipeline for Salesforce Marketing Cloud data for CRM data analysis. Implemented version control and automation of SSIS packages through BIML scripting;
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Built CRM campaign performance dashboards in Power BI, creating cost effectiveness, conversion rate, NPS, amongst other KPIs;
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Performed and enabled deep-dive analysis over CRM campaign performance, empowering marketing efforts. Insights contributed to a 30% increase in click conversion rate YoY;
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Implemented and communicated various best practices regarding query performance and naming schemes within team’s SQL Server data warehouse.
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Became point of reference for Qlik Sense development - built Brazil’s KPI dashboard, which was popularized globally. Usage of advanced Set Analysis and Qlik scripting;
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Created and conducted Qlik Sense training sessions for internal users;
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Development and maintenance of SSIS pipelines, delivering key tables used for credit risk management to the data warehouse;
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Created a dynamic sales reporting tool to support management, with a Power Query (M) / Power Pivot (DAX) based data model in Excel;
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Member on the winning team of a global innovation competition. Created and presented a pitch to the company’s executive board. Awarded with EUR 125k to implement the idea.
Projects
A project built to answer the age-old question: who is the best rapper?
This project was conceived through a combination of my fandom of hip hop and my desire to learn and practice some software and data engineering skills.
A list of hip hop / rap artists are compiled with certain criteria from the Spotify API and loaded to a Supabase database.
Through a Flask web app, users are prompted to choose between two artists in a head-to-head matchup. With the matchup results, the data is extracted to GCS and loaded to BigQuery.
Staging and mart models are built and documented via dbt, then metrics are visualized in a Power BI dashboard. A local instance of Airflow orchestrates the ELT pipeline.
Overall, building this project has helped me bridge the gap between my BI centered background and the skillset of the modern analytics engineer (plus some web dev basics!), while hopefully settling some music related debates.
Education
Federal University of Rio Grande do Sul (UFRGS)
Industrial Engineering (B.Sc.)
University of Wisconsin-Madison
Industrial Engineering (Exchange program)
Miscellaneous
- Proudly owned by two standard issue cats - see /r/StandardIssueCat;
- Mostly listen to hip hop and rap (hence the battle rap project);
- Mechanical keyboard enthusiast (and tech gadgets in general);
- Maxed on Old School Runescape.
