San Jose, California, United States
Experienced in building data engineering capabilities at organizations ground up (data infrastructure, ETL/ELT pipelines, Data Platform & Analytics). Previously built and led Data Engineering at Pinto playing a pivotal role in an acquisition. Currently building data engineering at Sourcegraph.
Founding Data Engineer at Sourcegraph, leading the company’s data engineering and analytics infrastructure. I lead the company’s end-to-end data platform, including the data warehouse, data infrastructure, and internal and external reporting systems. I have designed and operate the pipelines, warehouse architecture, and data tooling that power analytics, product insights, and decision-making across the organization. Enabling teams across the company to make better decisions through reliable data.
Founding Data Engineer. Led & Built Data Engineering Org. (Acquired)
Pinto is building a smarter food data platform, designed for the needs of today's consumers in the world of personalized diet. Hired as the first Data Engineer I work on the data platform team, I focus on building data engineering within Pinto and the three buckets of my work are: 1. Infrastructure -> Implementing best-practices and introducing core data engineering technologies (apache-spark, docker, data warehousing capabilities) 2. ETL / ELT Pipelines -> building fault-tolerant and highly scalable pipelines to deliver our data to clients 3. Data Platform & Analytics -> integrated business intelligence tool (redash.io) to allow users company-wide to query, build alerts and dashboards. In addition, I spent a good chunk of time building out our analytical product offerings Specific work duties include: - Streamlining the flow from multiple data sources to create real-time dashboards for top grocery retailers (Whole Foods Market, Kroger) to power actionable items on consumer trends - Designing systems that improve the accessibility and scalability of the data platform & API - Creating Python scripts to automate work in Photo Processing, QA Checks, and data manipulation/extraction - Experience working with large, complex data sets from a variety of sources - Tools Used: Python(Pandas, PIL, Numpy, Pymongo, etc), Tableau, MongoDB, Internal APIs, Public API
I worked for the iPhone Quality Engineering Team to assist with the 2018 iPhone launch. This included a historic moment of three iPhones being released concurrently for the first time (iPhone Xs, iPhone Xs Max, iPhone R). My role foresaw the quality of software being dispatched on each iPhone model, and coordinating with SW teams to push critical fixes to minimize user impact/disturbance. I presented daily to upper management (VP level) on timelines of software fixes and plans going forward. Specific work duties include: • Led cross functional engineering teams during 2018 iPhone launch to gather technical findings and determine corrective actions on early field failures • Presented on a daily basis to upper management an overview of ongoing field failures and efforts to resolve them • Managed 50+ international contractors to coordinate root causing customer issues • Automated data reporting and trend forecasting by creating analytics dashboards in Tableau • Increased efficiency of existing processes by 35% by creating a tool (using Python, Pandas, NumPy and Internal API’s) to extract realtime data, metrics and KPIs on iPhone health
Ritual is an order-ahead food application, that has partnered with 1000s of restaurants in Canada and the United States to provide an incentive based pick-up option for consumers. During my time at Ritual, I had the opportunity to lead and create many of the early front-end visualizations that clients/merchants of the app would receive. The visualizations focused on descriptive analytics that would help clients understand their sales/revenue growth MoM on the app. Furthermore, I was able to work on and analyze campaign data (eats-week) during an explosive period of the company, here I had the unique chance to look at campaigns and understand KPIs on what makes a campaign successful and key cost-benefit deep dives. Specific work duties include: • Created real time dashboards highlighting Merchant’s performance in-app with sales and users trend analysis • Analyzed data from promotional campaigns and measured key factors that play vital roles in attracting new customers to the platform • Launched new merchants on the platform and provided training on how to use the application • Developed a logistic regression model in Python(Pandas, Scikit-learn) to predict if an item will be bought independently • Built & ran data queries for teams on MySQL using multiple joins, sub-queries, group by, CASE and aggregate functions Received "OUTSTANDING" performance rating from employer
• Played key role in migrating client transaction data to cloud database (Amazon Web Services) • Created Shell scripts to extract, transform, and load (ETL) data coming in from clients • Developed internal tool using Python to validate/compare data between two different databases or datasets • Performed deep data dive analysis on the CPG market and presented recommendations to C level of LoyaltyOne Received "OUTSTANDING" performance rating from employer