Chargebee is a subscription management platform, used by fast-growth businesses like FreshWorks, Okta, and Envoy to manage and automate their recurring billing and lifecycle operations.
What can you expect from the role?
Chargbee is looking to leverage the full capability of its data assets to create value for its customers for its next phase of hyper-growth. Chargebee is looking for an inspirational engineering leader for designing, building and productionizing technical solutions to collect, manage, store and visualize data from a wide range of sources in scalable , performant and highly available manner. This role will lead engineering teams and partner with Product Mgmt and Business Analysts to enhance the quality and outcomes of the data products at Chargebee. In this role, you will lead building predictive analysis using AI/ML modules to leverage these datasets effectively.
Reporting to Vice President of Engineering, the key responsibilities include:
As Engineering Director of Data Reporting and Analytics , you will build, inspire, and grow talented engineering teams that are responsible for designing and building Data Engineering and Analytics suits for Chargebee’s Subscription Management Platform.
You will be responsible for creating and managing a complex and globally distributed data engineering pipeline at scale and manage its availability, throughputs, and security.
You will be responsible for managing product milestones, deployment cycles & delivery of the overall Data Engineering and Analytics roadmap
You will work with product management and engineering teams to influence key decisions on architecture and implementation of scalable, reliable, and cost-effective engineering solutions.
You will be expected to bring thought leadership to advance the overall state of technology and customer focus for the entire engineering organization.
What do we expect?
12-15+ years of demonstrated experience leading large teams including managers in remote and distributed product engineering setup.
5-8+ years of proven experience in areas of Analytics/Data Science/Data Engineering or Quantitative fields in hyper-scale environment
Accomplished practitioner of Agile and DevOps practices with experience guiding teams through planning, development, rollout and migration.
Ability to translate data and technical concepts into requirements documents and user stories for Data engineering as well as Data science.
Demonstrable Success managing teams in rapid product development, including remote and offshore teams
Demonstrated success building culture of innovation, ownership, accountability, and customer focus
Track record of building and scaling a SaaS product
Good communication and presentation skills with ability to interact with different cross-functional teams varying levels
Ability to learn new tools and paradigms in data engineering and science. Intellectually curious and continuously striving to learn.
Solid Experience in creating large scale data engineering pipelines, data-based decision-making and quantitative analysis.
Working knowledge in ETL/ELT data pipeline design, development and performance tuning in Big Data ecosystem.
Experience working with Data warehousing tools, including DynamoDB, SQL, Amazon Redshift, and Snowflake
Experience architecting data products in Streaming, Serverless and Microservices based Architecture and platform.
Exposure in building global scale cloud-native systems and modern tech stack: AWS, Java, Spring Framework, RESTful API, and container-based application.
Working knowledge of Data warehousing, Data modelling, Governance and Data Architecture
Experience of any industry standard ETL/workflow Tools (Mulesoft/Infa/Talend/airflow etc) and BI visualisation tools (Tableau, Looker etc)
Exposure in building Predictive models using machine learning through all phases of development, from design through training, evaluation, validation, and implementation.
Exposure with complex, high volume, multi-dimensional data, as well as AI/ML models based on unstructured, structured, and streaming datasets
Experience creating/supporting production software/systems and a proven track record of identifying and resolving performance bottlenecks for production systems.
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