Key Achievements- Provided database design, onboarding, and ad-hoc consulting for the data ops team to build data artifacts within Azure Databricks, and provisioned access through the Databricks Unity Catalog.- Maintained HTTP endpoints (Azure Durable Functions) for the data ops team to schedule downstream pipelines based on conditions in Jira. This allowed the team to fully automate pipelines, starting with ingestion and artifact creation (Databricks), proceeding through internal review (Jira), and ending with post-processing and delivery (Databricks).- Designed and deployed an operational data store (ODS) containing modeled data (Azure Database for PostgreSQL) and developed several associated pipelines (Azure Functions). This solution provided internal applications with a tool to ingest, model, and deliver client data.- Established a ruleset convention to improve the transparency of processing changes into the ODS.- Wrote the CI/CD pipelines for the automated deployment of application-level and infrastructure-level changes to the ODS.

Key Achievements- Led a cross-functional team consisting of individuals from product, design, and frontend engineering to re-architect the client analytics page using serverless infrastructure (AWS QuickSight).- Secured an annual contract with Snowflake as our data platform.- Tested several data ingestion services (Fivetran, Stitch, Meltano, Airbyte), then ultimately built out a custom integration solution that saved the company > $40k/year compared with the most likelyexternal choice (Fivetran).- Utilized a containerized environment (AWS ECS) triggered by an orchestration layer (Amazon MWAA) for the integration pipelines that connected the principal engineering databases with the data warehouse (Snowflake).- Governed the data warehouse to provision data access for internal stakeholders.- Provided transparent, version-controlled data transformations using GitHub Actions and dbt.

Key Achievements- Monitored the ingestion of key data assets for the firm (e.g. credit card purchases, email receipts, IP address metadata).- Worked with other data analysts to troubleshoot issues and implement best practices within Apache Spark (AWS Databricks).- Facilitated communication and problem-resolution between upstream data vendors and downstream data stakeholders.- Built an anomaly detection service to validate source-level and team-level statistics.- Developed a customer segmentation algorithm to predict user demographics with unstructured text utilizing the Spark ML library.- Utilized container services (AWS ECS) to pre-process the ingestion of certain data assets.- Incorporated an open-source solution to publish an internal status dashboard, providing improved transparency of our data pipelines.

Key Achievements- Utilized large datasets to publish periodic and event-driven research on select public companies (e.g. Airbnb, Amazon, Blue Apron, Etsy, Grubhub, HelloFresh, Lyft, Shutterfly, Stitch Fix, Uber, and Wayfair).- Modeled data for KPI prediction (target <2% MAPE), cohort analysis, and transactional metrics.Migrated data pipelines from on-prem infrastructure to a cloud-based environment (AWS Databricks).- Optimized the query and modeling process by implementing a modular approach to calculating key metrics.- Developed an Excel API tool that allowed our clients to download and interact with our API data (VBA).- Took ownership of new associate training to ensure the team's long-term success.

- Managed client relationships for a luxury furniture brand.

- Processed and classified mortgage servicing data.- Filed relevant claims with insurance companies.

- Compiled daily pricing information for U.S. debt securities.- Designed monthly revenue report of product and location data.- Produced market research on key relationships in the Asia-Pacific region.