Jan 2024 – Jan 2025 · 1 yr · Hyderabad, Telangana, India · Hybrid

• As a Staff Application Engineer at Uber, I rejoined to support the U4B (Uber for Business) Channel Allocation functions - owning the engineering behind metrics and numbers reported to the quarterly market, with accountability for accuracy, simplicity, and trust in the data.
• Key areas of impact:
• Engineered and maintained Channel Allocation systems powering quarterly market reporting - ensuring numbers were accurate, auditable, and stakeholder-ready
• Simplified complex allocation logic and legacy solutions into cleaner, more maintainable systems - reducing operational overhead and lowering the risk of error
• Accountable for end-to-end data accuracy across reporting pipelines, with KPIs around correctness, freshness, and reconciliation against source systems
• Partnered across tech, operational, development, and business management teams - translating business requirements into engineering solutions and bridging gaps between functions
• Acted as a connective tissue between functions, ensuring engineering decisions aligned with operational realities and business goals
• Drove improvements in observability, monitoring, and data validation to safeguard the integrity of market-facing metrics
• Mentored and collaborated with peer engineers, contributing to engineering standards, code quality, and team velocity
• Brought a strong team-player mindset to a cross-functional environment - balancing pragmatism with technical rigor in a high-stakes reporting context
• Operated at the intersection of engineering, operations, and business reporting - turning complex allocation logic into simple, accurate, and trustworthy systems.

Jul 2023 – Nov 2023 · 4 mos · Fremont, California, United States · Hybrid

• As part of Tesla's Center of Excellence (CoE), I led the design and delivery of People Tech solutions — building performance and operational processes that scaled across a global workforce of 86K+ employees spanning the USA, China, and UAE.
• Key areas of impact:
• Designed and launched an automated performance review cycle that saved 22 hours per employee per half-year, roughly 3.8M hours saved annually across Tesla's global workforce
• Architected and delivered People Tech platforms on Workday, supporting performance management, workforce operations, and employee lifecycle processes
• Defined KPIs and success metrics for HR and operational systems — adoption, process efficiency, data quality, and user experience across 86K+ employees
• Drove roadmap and prioritization for CoE initiatives, aligning engineering execution with broader people strategy and global business goals
• Reengineered legacy operational workflows into scalable, automated, data-driven processes deployed across multiple geographies
• Partnered with HR, business, and engineering stakeholders to translate complex people-process requirements into Workday-based technical solutions
• Led cross-functional teams through discovery, build, and rollout — ensuring smooth adoption across the USA, China, and UAE
• Established standards and best practices within the CoE to drive consistency, reusability, and long-term platform health
• Operated at the intersection of people, process, and platform — turning operational complexity into systems that scale globally.

Apr 2023 – Dec 2023 · 8 mos · United States · Remote

• As Engineering Lead, I built and scaled the Onyx platform; a research-grade data platform supporting scientific work on cancer and other diseases - enabling researchers, data scientists, and business teams to access trusted data without friction.
• Key areas of impact:
• Led the end-to-end setup of the Onyx platform for research-level data, enabling secure, governed access to sensitive datasets supporting cancer and broader disease research
• Drove the migration from on-premises infrastructure to Azure, modernizing the data stack for scalability, performance, and cost efficiency
• Designed and implemented a graph database powering the data catalogue and end-to-end data lineage — replacing manual discovery effort with self-service insight for business and research users
• Empowered non-technical business users to find, understand, and trust data independently — eliminating long-standing friction and bottlenecks
• Managed 3 engineering teams across the platform, owning hiring, mentorship, delivery cadence, and engineering standards
• Partnered closely with data scientists, researchers, and cross-functional stakeholders to translate scientific and analytical needs into robust platform capabilities
• Defined platform KPIs and success metrics — data discoverability, query performance, lineage coverage, adoption, and user satisfaction
• Established engineering best practices around data governance, observability, and reproducibility critical for research-grade workloads

May 2022 – Mar 2023 · 10 mos · San Francisco Bay Area

• Successfully developed and implemented cost-saving measures for data engineering operations, resulting in a 45% reduction in expenses
• Created a data-as-a-service model that cut down Snowflake annual costs from $400K to $215K
• Architected and implemented Apache Airflow on Kubernetes to provide access to key business insights and data-driven decision-making for multiple teams
• Implemented CI/CD using ArgoCD to improve the efficiency and effectiveness of data pipeline deployments
• Led the creation of a data foundation layer from scratch, including designing and building data pipelines using Apache Airflow
• Ensured the security and compliance of data engineering systems through maintenance of data governance policies and standards
• Actively evaluated and implemented new data engineering technologies and tools to support business objectives
• Regularly presented data engineering updates and progress to leadership, promoting effective communication and alignment with company goals
• Continuously monitored and analyzed data engineering performance metrics to identify and resolve issues and improve overall system performance
• Continuously assessed and improved data engineering team processes, methodologies, and best practices to ensure high-quality results
• Collaborated with cross-functional teams such as data science, providing mentorship and guidance to support professional development and growth.
• Built real-time data pipelines for workout reps and activities using Kafka for data lake from Tonal device.
• Maintained the organization's AWS account, including setting up and configuring new resources and monitoring usage and costs
• Managing Sagemaker for ML Data Models deployment and assisting data scientists
• Working with offshore teams in Samara, Russia

Jul 2018 – Jul 2022 · 4 yrs · San Francisco Bay Area

• Persistently collecting user requirements from various stakeholders to convert those to technical solutions
• Developed highly scalable engineering platform that serves redundant development
• Built new data models that provide intuitive analytics for Uber’s analysts and customers using - Hadoop/hive, Vertica, Presto, Impala, and other data warehouse technologies
• Optimized high resource consumption queries that are created by Analysts
• Extracted unstructured data from raw logs into multiple tables as per the optimal data model design
• Improved the data stability by re-engineering the obsolete pipelines and processes
• Created robust tasks for existing ETL pipelines that are ingesting data from various external sources to - Data WareHouse using Piper (A python client focused framework), Medley (A java framework), Kerby (A Kafka Framework) and Data Science Workbench (A python supported Jupyter Notebook)
• Discovered and redesigned bottlenecks of the slow performing pipelines, heavy resource (memory) consuming areas, redundant data transformation portions, humongous data extraction queries
• Improved the data quality by adding necessary check points and alerting mechanics
• Working with India, Philippines and Netherlands team on project milestones
• Mentored cross functional teammates on advanced distributed platform engineering

Oct 2016 – Jul 2018 · 1 yr 9 mos · Mountain View, California

• Involved in the Cloudera (CDH) 18 node hadoop cluster design & installation on Amazon EC2
• Worked as a release manager for the production jobs deployment using Git and Jenkins.
• Provisioned and Managed Hadoop Cluster in AWS
• Administered the Hadoop cluster for scaling up and down
• Implemented the role-based security through Sentry, Kerberos and ACLs
• Involved in extracting Big data from various data sources into Hadoop HDFS. This included data from CSV, Excel, XML, JSON, API systems, databases and also log data from servers.
• Created robust, resilient and scalable ETL pipelines using Spark, Apache Kudu
• Installed, configured and also written Hive UDFs.
• Responsible for reviewing end-user/BI HiveQL queries, rewriting them in spark to improve performance
• Created validation framework using python flask to report the data freshness from all the transactional tables
• Managed the Firewall requests from all the users to adhere the compliance rules
• Constantly support the business users and with their errors from Impala, Hive, Hue

Aug 2016 – Oct 2016 · 2 mos · Mountain View, California

• Designed and developed ETL frameworks for FTFCU's big data initiative. Which involves extracting data from different source systems, load into Hadoop Data Lake, pull data from Data Lake (HDFS), transform and load data into Enterprise Data Warehouse
• Designed an analytics engine which processes data to understand user behavior to identify patterns and makes product recommendations (Machine Learning). The reports generated help FSR's (Financial Service representative) to communicate with their customers regarding NBO (Proposing Next Best Offer to customers based on customer behavior and portfolio) and NBA (Next Best Advice), Member Journey etc

May 2016 – Aug 2016 · 3 mos · Oakland, CA

• Created Functional requirement specifications with the Scope analysis, Process modeling, Prototypes and data dictionaries with Cross functional team and doing the validation of cases and its implementation for Birth and Mortality projects
• Implemented IBM Quality Stage for probabilistic data quality for unified data mart

Feb 2015 – Mar 2016 · 1 yr 1 mo · Sandton

• Extracted the data from Oracle, Teradata and loaded into HDFS using Sqoop.
• Developed Hive (version 0.10) scripts for end user / analyst requirements to perform ad hoc analysis
• Developed Talend workflow for scheduling and orchestrating the ETL process

Aug 2014 – Feb 2015 · 6 mos · Midrand

• Managed team of six and prepared Technical Design Documents and Lower level Design documents such as UML diagrams, Activity and Process flow diagrams for the technical specs using visio
• Prepared the Implementation plan for the code migration to QA/Production.
• Responsible for building scalable distributed data solutions using Hadoop.

Nov 2013 – Jul 2014 · 8 mos

• Managed team of eight and created the FSLDM data model for Loyalty subject area
• Created logical and physical data models and ER diagrams using ERwin for the OLTP application
• Conducted standup meetings and provided enormous support to the various business sectors
• Involved in creating Business case and Functional requirement docs for Hadoop Ecosystem
• Created Cognos dashboards on top of HDFS for VIP customer lifestyle analysis

Feb 2010 – Nov 2013 · 3 yrs 9 mos · City of Johannesburg, Gauteng, South Africa

• Created and enhanced relational/DMR/OLAP metadata Framework Manager Models to support business requirements through Cognos tool
• Automated number of Excel Dashboards and Reports in SSRS and Cognos for business sectors such as Retail International Banking, Workplace Banking, Business Banking and Retail Banking
• Designed data marts for sales department, finance department and implemented the daily reporting system (extensively used T-SQL for ETL ((SSIS), (DTS)

Aug 2008 – Jan 2010 · 1 yr 5 mos · Sunninghill

• Designed and developed reporting tools for Hospitals to track appointments, check patient Medicare & Medical eligibility, resources availability etc... This helped hospitals to measure resource allocation and schedule appointments
• Worked extensively on Data warehousing, extensively used DataStage an ETL tool to design mappings to move data from Source to Target database-using Stages.
• Automation of ETL processes using DataStage Job Sequencer and Transform functions.