Lead/Staff Engineer - Data & MLOps

About Credit Saison India:

Established in 2019, CS India is one of the country’s fastest growing Non-Bank Financial Company (NBFC) lenders, with verticals in wholesale, direct lending and tech-enabled partnerships with Non-Bank Financial Companies (NBFCs) and fintechs. Its tech-enabled model coupled with underwriting capability facilitates lending at scale, meeting India’s huge gap for credit, especially with underserved and under penetrated segments of the population.
Credit Saison India is committed to growing as a lender and evolving its offerings in India for the long-term for MSMEs, households, individuals and more. CS India is registered with the Reserve Bank of India (RBI) and has an AAA rating from CRISIL (a subsidiary of S&P Global) and CARE Ratings.
Currently, CS India has a branch network of 45 physical offices, 1.2 million active loans, an AUM of over US$1.5B and an employee base of about 1,000 people.
Credit Saison India (CS India) is part of Saison International, a global financial company with a mission to bring people, partners and technology together, creating resilient and innovative financial solutions for positive impact.
Across its business arms of lending and corporate venture capital, Saison International is committed to being a transformative partner in creating opportunities and enabling the dreams of people.
Based in Singapore, over 1,000 employees work across Saison’s global operations spanning Singapore, India, Indonesia, Thailand, Vietnam, Mexico, Brazil.
Saison International is the international headquarters (IHQ) of Credit Saison Company Limited, founded in 1951 and one of Japan’s largest lending conglomerates with over 70 years of history and listed on the Tokyo Stock Exchange. The Company has evolved from a credit-card issuer to a diversified financial services provider across payments, leasing, finance, real estate and entertainment. 

Roles & Responsibilities:

Promote DataOps approach to Data science, engineering and analytics delivery processes to automate the provision of data, testing and monitoring and shorten CI/CD.

Collaborate with data & ML leads and create and build optimal data pipeline architecture for the data solutions including data science products

Ensure the data pipelines are scalable and performant as well as creating and maintaining service to connect data products

Create dashboards and other tools required to efficiently monitor our data and ML infrastructure, pipelines, ETL and analytics delivery processes. 

Building end-to-end event instrumentation and alerting system to detect and alert any anomaly in the system or in the data

Assist in managing our data and ML infrastructure (upgrading, monitoring, optimising)

Collaborate with IT DevOps engineers and participate in enterprise DevOps activities.

Exchange your knowledge on infra and data standards with other developers and be part of our tech community. Promote the use of engineering best practices.

Contribute to innovative POCs with our data & engineering teams.

Remain flexible towards technology approaches to ensure that the best advantage is being taken by new technologies.

Required skills & Qualifications:

Strong drive to solve problems, communicate clearly and contribute positively to a DevOps/DataOps culture

Knowledge of the latest DevOps tools and practices.

Experience with data pipelines within AWS (Glue, DataPipeline, Athena, EMR, DMS, Spark)

Experience of Database Replication and databases like Aurora, MySQL, MariaDB, etc.

Efficient in building CI/CD pipelines for containerized Java/Python codestack

Comfortable with Git workflow.

Experience with applications deployed in AWS.

Experience with configuration management and provisioning tools (e.g., Ansible, CloudFormation, Terraform)

Knowledge of one or more scripting languages  - Bash/Python/JavaScript

Orchestration/containerisation using Docker and Kubernetes.

Basic knowledge of data science & ML engineering

Bachelor's Degree in computer science or similar degree or Big Data Background from top tier universities.

Experience: 6 - 10 years of experience.