Leadership Role - Data Engineering

About the Role :

- As the leader of the Data Engineering org , you are tasked to Grow, lead, and mentor a team of data engineers.

- Own data strategy for , work with stakeholders to buildout state of the art data platform.

- Work with data science and business stakeholders to deploy scalable data pipelines and machine learning models in production in a timely manner.

Responsibilities :

- Responsible for full software life-cycle, system design and development of front-end & back-end systems

- Writing high-quality code, participating in code reviews, designing/architecting systems of varying complexity and scope.

- Identify libraries and technologies worth experimentation

- Build innovative solutions from scratch and liaise with architects and engineers to build solutions

- Mentoring other team members Required Skill

- Degree in Computer Science or relevant experience

- Work in a fast-paced environment and make pragmatic engineering decisions in a short amount of time.

- Experience with Agile Development and Scrum methodologies

Requirements :

- 7+ years of relevant hands-on software engineering experience doing software design and development

- Excellent understanding of relational database structures, having knowledge of unstructured databases (NoSQL) will be an added advantage

- Proven experience of working on back-end web frameworks like RoR (preferable) or Python/Django or Node.js

- Expertise in object-oriented design, unit testing, integration testing, data structures, algorithms, scalable APIs, etc.

- Knowledge of working on cloud technologies and exposure of AWS services (EC2, RDS, S3, etc)

- Design and implement a self-service platform to speed up development and automate the deployment and scaling of data pipelines and ML models in production.

- Assemble large, complex data sets that meet requirements to support data science and analytics projects from external and internal sources.

- You will be responsible for managing product milestones, deployment cycles & delivery of the overall Data Engineering roadmap

- Responsible for architecting and managing the infrastructure to support all stages of the machine learning model lifecycle, including feature engineering, feature store, model training, testing, monitoring, and deployment in a production environment.

- Build and maintain A/B testing infrastructure for analytics and data science experimentation.

- Proactively identify, design, and implement internal process improvements including automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability