Job Title

Analytics EngineerLocation: Mumbai. Full-timeExperience: 2–4 years in fintech, NBFC, banking, inside sales, tele-sales, or customer successAbout Apollo FinvestWe are a publicly listed tech first NBFC — think AWS, but for lending. Armed with advanced APIs and capital, we team up with the best fintechs to offer digital loans across the country. It’s where finance meets tech, with a splash of innovation, with a strong focus on innovation and customer experience.Check out Apollo’s journey here! Apollo Cash is our digital personal loan app designed to provide fast, seamless access to credit — entirely through a mobile journey.Role SummaryThis 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 RoleEnsure 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 featurecreation, policy building, and portfolio monitoring. Core Responsibilities1. End-to-End Data OwnershipDesign, 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 → storageWork closely with Tech, Product, and Data Science to define what data should be capturedMaintain data documentation, data dictionaries, and schema governance Ensure data quality, consistency, and version control2. 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)Convert JSON, logs, and raw API responses into structured feature tablesBuild regex/keyword-based parsers for financial SMS classification3. Feature Implementation (From Risk &;Data Science Team)Implement feature creation logic provided by Risk/Data Science teamTranslate business and policy logic into SQL/Python pipelinesCreate reusable feature layers for underwriting, fraud, collections, and monitoringMaintain a feature store for consistent model and policy usage4. Lending Data Understanding (Domain-Specific Requirement)Work with Bureau dataStructure SMS-derived financial variables (income, stress, EMI signals)Work with Account Aggregator and bank transaction datasetsUnderstand fintech alternate data used in underwriting and fraud detection5. Data Pipelines & AutomationBuild and maintain ETL/ELT pipelines using Python & SQLCreate cron jobs for automated data ingestion and feature refreshAutomate vendor data pulls (Bureau, SMS SDK, AA, device data)Ensure low-latency pipelines for real-time underwriting use cases6. Database Structuring & Storage ArchitectureStructure clean datasets in PostgreSQL (analytics layer)Manage raw data storage in DynamoDB / S3 data lakeDesign normalized and denormalised tables for risk analyticsOptimise database performance for large-scale query workloads7. Dashboards & Readable Data LayerCreate analytics-ready datasets, implement & write Metabase queries and convert it into dashboards (Metabase / Power BI )Enable self-serve data access for Risk, Business, and FoundersSupport ad-hoc analysis requirements from leadership8. 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 SERVICESPostgreSQL (Primary analytics DB)DynamoDB (Raw/NoSQL storage)Python (Pandas, NumPy, ETL frameworks)Advanced SQLAPIs, JSON, and Log Data HandlingMust-Have Skills2–6 years experience in Data Engineering / Analytics Engineering / Fintech Data rolesStrong Python and SQL (production level)Experience handling unstructured data (SMS, logs, JSON, APIs)Experience building data pipelines, schedulers, and cron jobsStrong database design and data modelling skillsAbility to work in a startup environment with high ownershipFamiliarity with modern platforms like Snowflake, Google BigQuery, or Amazon Redshift Good to Have (Highly Preferred)Experience in Lending / NBFC / Fintech domainExperience working with Bureau, SMS, Device, or Banking dataExperience with streaming (Kafka/Kinesis) and orchestration (Airflow or Step Functions)Experience with feature stores and risk analytics datasetsKnowledge of regex, NLP basics for SMS parsingExperience supporting real-time decision engines / underwriting systemsWhy This Role is Mission-Critical for Apollo Cash : Apollo Cash captures raw SMS, Device, Bureau, AA, and App behavioural data at scale.Without proper structuring and pipelines, risk models, fraud rules, dashboards, and policiescannot function effectively. This role will be the single owner responsible for transformingraw lending data into a clean, usable, and production-ready data layer poweringunderwriting, fraud detection, portfolio monitoring, and business analytics.