Bhavika Faldu

About Bhavika Faldu

Building Sales Data Mesh at Salesforce! Let’s make AI more reliable and secure by fueling it with first-class data. Bhavika is a FULL STACK Data Engineer with 7+ years of experience in translating complex business needs into scalable, trusted data models and metrics. She specializes in leveraging cloud platforms to create self-service solutions for Analytics and Data Science teams. Known for bridging the gap between technical and non-technical stakeholders, Bhavika has strong storytelling skills and a proven record of presenting at the Tableau Conference. A high-energy collaborator, she brings clarity, momentum, and positive energy to every project.At Salesforce, she led large-scale migrations (Tableau to Salesforce M&A Migration, Airflow to DBT migration, Workday Data Migration), architected core Sales data models, Headcount Data model and built frameworks for self-service platform to enable Analytics and Data Science teams supporting across Sales, Finance, and Operations. She also created reusable infrastructure for change data capture named Data Déjà vu, implemented enterprise-wide Data Governance practices including Row-Level Security, and also presented her work related to Headcount Insights at Tableau Conference 2024. She has expertise in creating Data extraction services using cloud services. While working on foundational team, she had opportunity to set up CICD, create data quality and monitoring frameworks, and various processes to improve team wide efficiency.Previously at PwC, Bhavika designed and deployed a self-service data extraction platform using Python, FastAPI, and Docker on Kubernetes—onboarding 23+ new client. She automated ERD generation, built data quality validators, and implemented pipeline alerting systems, reducing manual effort by 100% and release delays across complex ERP systems.Known for bridging technical and non-technical teams, mentoring others, and thriving in cross-functional environments, Bhavika believes great data engineering is as much about communication as it is about code.Her technical toolbox includes but not limited to: DBT, Airflow, Snowflake, AWS, Tableau, SQL, Python, GoJS, Spark, Kafka, Docker, Kubernetes, Git, Jenkins, FastAPI, Fivetran, JIRA,and more. Tools are no bar for her given her zeel to keep learning new tools and technologies.If you’re looking for a passionate Data Engineer who is always excited to learn something new and looks forward to build scalable, reliable enterprise wide data solutions and help your teams unlock the full potential of their data, feel free to connect or reach out.

Experience

1. Data Engineer

   Salesforce

   Dec 2021 — Present · Seattle, WA, US

   Spearheaded migration of 80+ complex data pipelines during Tableau-Salesforce M&A, aligning 60+ Sales KPIs with Salesforce definitions by closely collaborating with business stakeholders.Supported end-to-end migration of 85+ pipelines from Salesforce Data Platform to dbt, co-architecting scalable data models now adopted as PoC by 8+ teams.Architected and maintained a critical Sales data source supporting 120+ Tableau dashboards and 4 data science models, serving sales reps and leaders with actionable daily insights.Built and migrated the Headcount Data Source from Anaplan to Workday — now Salesforce’s trusted Headcount SSOT with high accuracy and minimal maintenance.Presented \"Headcount Insights Unleashed with Tableau\" at Tableau Conference 2024 to 500+ attendees, showcasing advanced modeling techniques.Created Data Déjà Vu — a reusable historical layer in Snowflake, enabling retroactive data correction within 2 days; now powering 5+ teams and influencing dbt’s native versioning.Designed a Self-Service Data Ingestion platform using AWS and Docker, reducing onboarding time to <3 days and accelerating urgent dashboard/ML requests.Delivered reliable, scalable data sources that helped drive Tableau adoption within Salesforce, contributing to our team’s integration with CRO Analytics.Established org-wide best practices for dbt pipelines — including CI/CD workflows, Slack monitoring, GitHub–Jira integrations, unit testing, and documentation — boosting delivery speed and reliability.Designed and implemented a dbt testing framework, improving trust and saving 6–8 hours/month in QA.Mentoring 3 analysts transitioning into data engineering by guiding them on SQL, dbt, and model ownership.

Education

• MCT's Rajiv Gandhi Institute Of Technology

  Bachelor of Engineering (B.E.), Computer Engineering

  2013 — 2017

• The University of Texas at Dallas

  Master's degree, Information Technology and Management

  2018 — 2020