Customer Sales Executive

Software Engineer

The Role

We are looking for a full stack engineer to join our team. We ideally hire engineers who are comfortable across the full stack, but

we know you will have a preference about being on the front-end or back-end. As long as you're happy to work on both sets of

tasks – you should carry on reading!

Our Technology

• Back-end: C#, ASP.NET, Web API, MVC, Entity Framework

• Database: SQL Server. Knowledge of non-SQL databases is a plus

• Cloud: Microsoft Azure, AWS

Responsibilities

• Design of the overall architecture of the web application

• Implementation of a robust set of services and APIs to power the web application

• Building reusable code and libraries for future use

• Optimization of the application for maximum speed and scalability

• Implementation of security and data protection

• Translation of UI/UX wireframesto visual elements

• Integration of the front-end and back-end aspects of the web application

Additiontionalresponsibilitiesfor Project Lead

• Active participation in design\build cycle of the software engineering life cycle (prototyping, architecture, detailed design,

development, testing and deployment).

• Providing expertise in technical analysis and solving technical issues during project delivery.

• Code reviews, test case reviews and ensure code developed meets the requirements.

• Collaborate with product management and engineering to define and implement innovative solutions for the product

direction, visuals and experience.

• Requirement gathering and understanding, analyze and convert functional requirements into concrete technical tasks and able

to provide reasonable effort estimates

• Mentor and develop skills of junior software engineers in the team.

Tech Skills and Qualifications

• Contract Length : 1 year

• Software Engineering Degree with 3-5 years of experience.

• Expert knowledge of JavaScript and Node.js, good understanding of Angular and JavaScript testing frameworks (such as Jest,

Mocha etc.)

• Good understanding of Cloud Native architecture, containerisation, Docker, Microsoft Azure/AWS, CI/CD, and DevOps

culture.

• Knowledge of cloud-based SaaS applications/architecture.

• Practical experience in the use of leading engineering practices and principles.

• Practical experience of building robust solutions at large scale.

• Appreciation for functions of Product and Design, experience working in cross-functional teams.

• Understanding differences between multiple delivery platforms (such as mobile vs. desktop), and optimizing output to

match the specific platform.

Must-Have Skills

• Minimum 3 years of experience in Data Engineering / Analytics Engineering / Fintech Data roles
• Must have worked on SMS Parsing, intelligent platform, converting RAW customer SMS data into structured actionable financial signals and enabling downstream usage of SMS derived variables
• Must have established a continuous learning cycle to expand parser coverage
• Experience in Lending / NBFC / Fintech domain
• Experience working with Bureau, SMS, Device, or Banking data
• Strong Python and SQL (production level)
• Experience handling unstructured data (SMS, logs, JSON, APIs)
• Experience building data pipelines, schedulers, and cron jobs
• Strong database design and data modelling skills
• Ability to work in a startup environment with high ownership
• Familiarity with modern platforms like AWS, Snowflake, Google BigQuery, Redshift

Good to Have

• Experience in STPL, especially less than 25K ticket size
• Experience with streaming (Kafka/Kinesis) and orchestration (Airflow or Step Functions)
• Experience with feature stores and risk analytics datasets
• Knowledge of regex, NLP basics for SMS parsing
• Experience supporting real-time decision engines/underwriting systems

Role Summary

This role will be responsible for owning the end-to-end data-structuring layer across the organisation. The individual will transform large volumes of raw, unstructured, and semi-structured data (such as SMS, device, bureau, and app data) into clean, standardised, and analysis-ready datasets. These structured datasets will directly power risk analytics, fraud detection, marketing insights, collections strategy, and policy decisioning.

Key Objective of the Role

Ensure all raw lending data (SMS, Bureau, Device, AA, App logs) is captured, parsed, structured, and stored in a clean analytics-ready format inside databases (PostgreSQL, DynamoDB, AWS stack) so that the Risk and Data Science team can directly use it for feature creation, policy building, and portfolio monitoring.

Core Responsibilities

1. End-to-End Data Ownership

• Design, build, and maintain end-to-end data pipelines (batch + streaming) using AWS native services (Glue, Lambda, Step Functions, Kinesis, S3, Athena, Redshift, EMR/Spark, etc.): ingestion

→ parsing → structuring → storage

• Work closely with Tech, Product, and Data Science to define what data should be captured
• Maintain data documentation, data dictionaries, and schema governance
• Ensure data quality, consistency, and version control

1. Unstructured Data Processing (Highest Priority)

• Parse raw SMS dumps and categorise into salary, EMI, loan apps, collections, credits, debits, OTP, etc.
• Process device fingerprint, behavioural logs, and vendor data (FinBox, AA, Bureau APIs)
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• Convert JSON, logs, and raw API responses into structured feature tables
• Build regex/keyword-based parsers for financial SMS classification

1. Feature Implementation (From Risk & Data Science Team)

• Implement feature creation logic provided by Risk/Data Science team
• Translate business and policy logic into SQL/Python pipelines
• Create reusable feature layers for underwriting, fraud, collections, and monitoring
• Maintain a feature store for consistent model and policy usage

1. Lending Data Understanding (Domain-Specific Requirement)

• Work with Bureau data
• Structure SMS-derived financial variables (income, stress, EMI signals)
• Work with Account Aggregator and bank transaction datasets
• Understand fintech alternate data used in underwriting and fraud detection

1. Data Pipelines & Automation

• Build and maintain ETL/ELT pipelines using Python & SQL
• Create cron jobs for automated data ingestion and feature refresh
• Automate vendor data pulls (Bureau, SMS SDK, AA, device data)
• Ensure low-latency pipelines for real-time underwriting use cases

1. Database Structuring & Storage Architecture

• Structure clean datasets in PostgreSQL (analytics layer)
• Manage raw data storage in DynamoDB / S3 data lake
• Design normalized and denormalised tables for risk analytics
• Optimise database performance for large-scale query workloads

1. Dashboards & Readable Data Layer

• Create analytics-ready datasets, implement & write Metabase queries and convert into dashboards (Metabase / Power BI)
• Enable self-serve data access for Risk, Business, and Founders
• Support ad-hoc analysis requirements from leadership

1. Cross-Functional Collaboration (Very Important)

• The role requires close collaboration with data science, tech, product, and business teams to ensure reliable data pipelines, well-defined schemas, API integrations, logging architecture and high data quality, enabling faster and more accurate decision-making across lending workflows.

Tech Stack (Current Environment)

• AWS Services
• PostgreSQL (Primary analytics DB)
• DynamoDB (Raw/NoSQL storage)
• Python (Pandas, NumPy, ETL frameworks)
• Advanced SQL
• APIs, JSON, and Log Data Handling

• 5–7+ years of experience designing and maintaining SQL queries, views, stored

procedures, and functions

• Strong experience with Oracle, Apache NIFI and/or PostgreSQL or a comparable relational

database

• Experience tuning SQL for performance, particularly in large-scale, high-volume

environments

• Familiarity with ETL development, data migration strategies, and real-time/batch

data movement

• Solid understanding of data security, particularly in PCI-compliant or regulated

environments

• Proven ability to work independently and within a team, managing priorities across

concurrent projects

• Strong written and verbal communication skills; able to work effectively with both

technical and non-technical stakeholders

• Bonus Skills:

o Experience with data mapping, profiling, or discovery tools

o Familiarity with Snowflake or other modern cloud data platforms

o Experience with job scheduling and automation (e.g., Cron, Automate

Schedule)

o Familiarity with Git or Bitbucket or other version control systems

o Experience working alongside or supporting Java-based development teams

Hiring : Technical Senior Salesforce Developer

Required skill set : Salesforce Apex and LWC

Experience : 5 to 8 yrs

Location : Bengaluru \ Hyderabad ( Hybrid )

Primary Skills :

• Salesforce Service Cloud / Sales Cloud
• Salesforce CRM/PRM Modules.
• Salesforce Apex, Visual force, LWC, Flows, Triggers, fields, page layouts, record types, custom settings, dashboards and reports
• Lightning (Aura, LWC), HTML, CSS, JavaScript
• Integration SOAP, REST, Bulk API), integration patterns, and best practices.
• Knowledge of Omni scripts is an added advantage.
• Knowledge of Salesforce PRM, Git hub
• MSSQL DB ,NodeJS ,Managed package experience ,Einstein Analytics and Community Cloud ,Good to have PD1 certification

Certifications :

• Salesforce Administrator (ADM-201)
• Salesforce Platform App Builder

Salesforce Platform Developer I (and II is a plus)

Other Skills :

• SFDC development and implementation experience.
• Experience in building Salesforce apps from development to support.
• In-depth understanding of the capabilities and constraints of the SFDC CRM/PRM applications.
• Ability to Identify and gather requirements from users and stakeholders.
• Analyze detailed business/functional/technical requirements and specifications for the application.
• Supporting application testing, deployment, maintenance, and evolution activities by correcting programming errors, responding to scope changes, and coding application enhancements.
• Knowledge of APIs and security settings.

Key Responsibilities:

• Install, configure, and maintain Hadoop clusters.

• Monitor cluster performance and ensure high availability.

• Manage Hadoop ecosystem components (HDFS, YARN, Ozone, Spark, Kudu, Hive).

• Perform routine cluster maintenance and troubleshooting.

• Implement and manage security and data governance.

• Monitor systems health and optimize performance.

• Collaborate with cross-functional teams to support big data applications.

• Perform Linux administration tasks and manage system configurations.

• Ensure data integrity and backup procedures.