Soumitro Tagore

Leading engineering for all aspects of core ads on Twitter (anything that runs on Twitter itself)- ads product offerings, ads platform/infrastructure, ads API, creatives infrastructure, billing/payments, traffic quality, ads safety, privacy, advertiser platform etc The software and platform my teams build are responsible for over $ 3 billion of revenue for Twitter Leading a large (300+) distributed team (across 7 sites as well as remote engineers) that is responsible for all aspects of revenue (ads, subscriptions etc) infrastructure and platform for Twitter. The components comprising the system are ads serving, advertiser analytics, analytics infrastructure, callback systems, subscriptions infrastructure, billing/payments, onboarding, campaign management, measurement/experimentation framework, targeting, audiences, attribution, APIs, traffic quality ( ads spam prevention) etc

Engineering executive in charge of growth (users, revenue) and data (metrics, infrastructure, governance and analytics) for the multi-billion $ Shopping business. I set up the growth charter, build the product roadmap and hired a team from scratch and then executed on the charter over 4 years. Managed a large team of engineers, data scientists/analysts. In charge of growth/marketing-tech/monetization (new ad formats, customer acquisition/retention, promotional platforms, affiliate programs) and data (engineering/governance/data science) for the Shopping business (Ads, e-commerce ). As a leader, I was responsible for driving the majority of revenue for shopping e-commerce Lead for all things related to metrics and data governance across 3 locations and multiple teams Primary focus areas: 1) User growth across all of shopping (e-commerce, ads) 2) New ads formats to support emerging parts of the business. These formats show on the Google SRP and developing them requires significant work across UI, data modeling/ML, user acquisition, ads campaigns, auctions, serving systems, feeds . 3) Data infrastructure - warehouses/pipelines, feature stores, experimentation frameworks 4) Data science/ML: a) Using ML to model/ optimize the business (personalization, retention/engagement, acquisition, LTV and channel efficiency modeling, fraud/risk modeling/prevention, predictive click through/conversion) b) For e-commerce business, I was overall responsible for the Data/Analytics platform ( Experimentation Framework, Telemetry, Data Warehouses, Data Governance, Analytical tools) . Also drove moving the products from pure batch systems to kappa architectures 5) Growing/Optimizing acquisition/engagement channels using ML 6) Owner of critical systems for notifications, mobile acquisition campaigns, promotions/loyalty, custom content 7) Overall engineering/XFN leadership for promo events, revenue management

Lead a team of about 150 engineers working on Google Cloud

Reporting in to CEO (VP of Engineering for the duration, Head of Product for first 6 months and CTO for last 6 months of my tenure). Building a scalable solution to compete with Tableau etc Executive in charge of the development, QE, devops and support (40 person strong) teams distributed across Menlo Park/Chicago for a startup of about 70 people. The AWS based big data analytics solution uses Apache Spark/Hadoop technologies to process large amounts of customer data and provide a mechanism for fast cycle data analytics, machine learning and data visualization. CSD was one of the earliest adopters of Spark and I was instrumental in helping advance our Spark usage/expertise during my tenure (working with both DataBricks/Intel) The CSD product is used by enterprises to detect, analyze and visualize business trends. The data sources supported include relational dbs, Hadoop/Hive based sources, Web and social application data sources, and third-party data . The CSD platform simplifies access to disparate data sources, automatically manages data harmonization, and enabled interactive analysis at scale. - Senior member of the executive team deeply involved in defining company strategy and managing operations - Managing product definition and product strategy (first 6-8 months) including managing the individual PMs - Deeply involved in the technology roadmap : helping drive the architecture of Spark layer enhancements including incorporating the latest version of SparkSQL, Parquet, streaming SQL, machine learning, data federation and query push-down. -Managed the AWS cloud infrastructure and worked with potential customers to allay their concerns around security (AuthN, AuthZ, SSO, security incident management), scalability/performance/reliability - Using Agile/kanban development process - Initiated the process of rigorous goal-setting for the different development teams using the OKR approach - Drove a high volume recruiting process to fill open reqs

Reported to the CEO. Clari is pioneering the new area of revenue operations , helping sales teams drive better sales execution and forecast accuracy by leveraging the power of artificial intelligence. I was head of engineering and in charge of engineering (dev and QA) and dev operations . I joined the company about 8 months after the Series A funding (total company size was 15 and engineering team of 9). The Clari product's primary use case is the use of ML and data science for sales deal predictions, insights and intelligence to sales teams on deals. The product is comprised of end-user (for sales reps) mobile apps (iOS/Android) and a web interface for sales operations/sales managers. The server component is deployed on Amazon AWS ( Java, PostGreSQL, Cassandra, Firebase) and is optimized for enterprise-level performance and scale. Clari integrates with Salesforce CRM, communication systems (e-mail/calendar) and data storage (Google Apps, Box, Dropbox etc ) to provide a unified view of all the information required to make sales professionals successful. - Senior executive deeply involved in defining company strategy - Participated actively in Series B fund raising. - Used an agile/train model to release targeted features once every 2 weeks. -Introduced and operationalized a feature team based-approach in engineering to closely align the engineering teams with the different target (sales reps, sales managers, sales operations) users and to deliver features optimized for them. Defined OKRs (objectives and key results) for each feature team and tracked the OKRs quarterly. - Built a recruiting machine that more than doubled the engineering team in a very challenging recruiting environment. - Worked closely with product management on analyzing customer usage (using MixPanel usage analysis) and used the data to drive app design and features.

Part of the broader leadership team for the $500 million in sales and 800 person in staffing (across three main sites – Mtn View, CA; London, UK; Pune, India) data protection business. Helped define product and business strategy for the entire group and cross-product integration efforts. Continued to manage and grow the Clearwell (at its peak almost 200 team members) engineering, QA, program management and docs team at Symantec. Built up from scratch a large offshore development team consisting of both developers and QA engineers. Offshore (Pune, India) team size was 75+. Delivered multiple major releases including significant functionality like distributed architecture, scalability (using big data technologies like Cassandra and Hadoop), machine learning solutions for automated document classification (using ML) and improved performance in spite of post-acquisition churn and attrition Helped manage the technology initiatives and engineering/engineering operations for the Enterprise Vault cloud product and integrated it with the Clearwell e-discovery product.

Reporting in to the CEO. Clearwell was the premier e-Discovery product on the market deployed as an enterprise Java application. The functionality it provides includes full-text indexing, enterprise full-text search, predictive analytics and review/viewing of large volumes of unstructured data. Technologies used include Lucene, MySQL and scaling technologies including Cassandra and memcached. Clearwell was funded by leading venture capital firms including Sequoia and RedPoint. I managed the Clearwell engineering/QA (across two sites, Mountain View, CA and Hyderabad, India) , program management, docs and technical support organizations over the period from May 2008 to the time of acquisition by Symantec in June 2011 Clearwell achieved annual revenue run rate of $ 100 million and team size off 300+ (was $2 million and 40+ when I joined) , and was acquired by Symantec in June 2011 for $500 million. During this time, I led the delivery of multiple major versions (seven) and innumerable fix packs for the core product. I also grew the team from about 20 engineers to 120+ engineers at the time of the acquisition (with most of the growth in 2010 when we grew from 25 to 80). I also led the technical support team and helped grow it from 1 engineer to 15 members (including a director of support) at the time of acquisition. I helped put all support processes in place (in Salesforce) and ensured the processes scaled with the team. Also helped defined the infrastructure and plans for the support knowledge-base. I was a key member of the Clearwell executive management team and helped define the company strategy and execution plans. Central role in the product strategy council and was instrumental in helping define the product roadmap/strategy as well as the release roadmap.

2006-2007 - Build a dev/qa team from scratch and led the development of the next-generation e-mail archiving/management system designed for high-scale and support for text-based analytics. 2001- 2006 - Technical manager and architect for the middle-tier components of IBM's Content Manager (CM) product. CM is an enterprise software product used for managing enterprise content in organizations. Enterprise Content includes web content, e-mail, documents, scanned images and collaborative content like blog and wiki content, instant messages, threaded discussions etc. Also involved in the standards work for JSR-170 (Java Content Repository)

Informix was a vendor of databases and data-access technologies. At Informix, I successfully led the definition and the delivery of multiple product releases of an object-relational mapping tool. I took on multiple responsibilities including Product Manager, Development Manager, Product Architect and Lead Software Engineer and made significant contributions in all of them. I also led the development of Data Director, an Active X/COM development tool that was the continuation of the work I did while at Centerview. Finally, I also led the effort to define Java based stored procedures in the database and made the corresponding changes to query optimizers to support these new additions 1999 - 2001 - Technical Manager/Architect/Lead Developer - Object Translator was an enterprise application development tool for Java and Windows (COM) developers targeted towards automating the development of object-relational, XML and web services applications. I made the business case for the tool, presented it to executive management (and got approval for proceeding) and then built the team from scratch and delivered the first 2 major releases. 1997-1999 - Architect/Lead Developer. Data Director was a Windows based data-model driven application development tool used to add complex data access to VB/ActiveX applications. I continued in the technical/team lead role after the acquisition and delivered multiple releases of the product over the next 2 years. Involved in the standards communities for Informix - was active and involved on SOAP/XMLP 1.0, JSR-12 (Java Data Objects), JSR-109 (Enterprise Web Services)