About me
Hello, I’m Javi. A data engineering leader with a master’s degree in Data Analytics. My journey into the world of data began with a curiosity for the insights hiding in raw information. Over time, I’ve developed a deep expertise in building infrastructure and scalable systems that transform those insights into business strategy.
My experience spans both startups and global organizations—including Lyft and RBI—where I led operational initiatives and helped teams unlock clarity through clean, usable data. I specialize in the technical side of data management and architecture, but I’ve always been driven by the end goal: helping people understand and act on data.
Outside of work, I’m a proud dog dad to six chihuahuas, and I have a lifelong appreciation for the arts and architecture. I love exploring new cities, museums, and anything with thoughtful design—old or new.
I believe great data work is about more than numbers. It’s about translating complexity into understanding and using that clarity to drive action.
Thanks for visiting my portfolio. Feel free to explore my projects or connect with me on LinkedIn, GitHub, Tableau, or Medium.
my experience
Over the past 7 years, I’ve progressed from operations into data engineering—earning a bachelor’s and master’s in data analytics, leading cross-functional initiatives at Lyft, and now managing infrastructure at one of the world’s largest QSR brands. My work spans data pipelines, experimentation design, stakeholder reporting, and platform automation.
Q&A
What sparked your interest in data engineering?
I’ve always been drawn to systems that solve real-world problems at scale. Early in my career, I found myself building data workflows that ingested, cleaned, and served insights to downstream tools so teams could make better decisions. That sparked a deeper curiosity about infrastructure, pipelines, and automation—and ultimately led me into data engineering. I enjoy creating solutions that not only work, but scale reliably and efficiently.
What tools and technologies do you enjoy working with most?
I enjoy working with tools that emphasize reproducibility, scalability, and simplicity. Python and SQL are foundational in my work, while dbt has become a go-to for transforming data reliably. I use Snowflake for warehousing, Terraform for infrastructure-as-code, and Airflow to orchestrate pipelines. I’m also comfortable with Docker and Git for version control and portability.
How do you approach building a new data pipeline?
I start by understanding the business use case—what data is needed, why, and how it will be consumed. From there, I define clear SLAs, source the data, and build ingestion with tools like Python or Airbyte. I prioritize modularity and observability in the transformation layer, using dbt or custom scripts. Pipelines are orchestrated via Airflow, containerized when needed, and version-controlled in Git to ensure traceability.
Can you share a data engineering project you’re especially proud of?
At RBI, I’ve reshaped how the operations team consumes and trusts data by designing and implementing a centralized performance pipeline in Snowflake. Using Terraform for infrastructure provisioning, I established a scalable, governed environment that now serves as the source of truth for key restaurant operations metrics. I led the standardization of business logic and transformed fragmented datasets into structured, queryable models that power dashboards used across the organization. To ensure adoption and continuity, I created extensive documentation outlining data definitions, transformation logic, and use cases—aligning cross-functional teams and setting a new standard for data consistency in operations that has spread across the organization.
How do you stay current in the rapidly evolving field of data engineering?
I stay sharp by combining hands-on practice with continuous learning. I explore documentation for tools like dbt, Snowflake, and Airflow, and regularly follow engineering blogs, newsletters (like Data Engineering Weekly), and GitHub repositories. I also take online courses, contribute to internal tooling improvements, and experiment with new technologies in sandbox environments.
What do you enjoy outside of work?
I’m a proud dog dad to six chihuahuas and enjoy spending time with family, especially outdoors. I also love visiting museums, studying design, and exploring how systems—whether architectural or technical—are built and maintained. That curiosity shapes how I think about engineering elegant, scalable solutions.
Curious about my work in practice? Check out my recent projects below.
J. Lopez Castillo 