Software Development Engineer II (Data Platform)
Urban Company
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Job summary
Qualifications
- 2-4 years of backend or data engineering experience with solid computer science fundamentals.
- Strong system design skills in microservice and data architecture.
- Familiarity with big data processing tools like Spark and MapReduce is a plus.
Responsibilities
- Design and build backend systems that support scalable data processing.
- Own systems end-to-end: from architecture to implementation.
- Work closely with data scientists to productionize ML workflows.
Skills
Tools
Job description
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Join to apply for the Software Development Engineer II (Data Platform) role at Urban Company
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Urban Company is Indiaʼs leading tech-enabled home services platform, operating across India, UAE, Singapore, and KSA. From salon and spa services at home to appliance repair, plumbing, and cleaning, we bring trusted professionals to customersʼ doorsteps through a seamless, tech-first experience.
We deeply believe in the power of AI to reimagine every facet of our operations from onboarding and customer support to service quality and personalization.
Some of our most impactful use cases include:
● Voice Bots using GenAI – Automates partner onboarding with natural conversations, ensuring speed and SOP compliance.
● Proof of Work – Uses GenAI to compare service images and assess quality at scale.
● Provider-at-Fault Detection – Analyzes chats and calls to identify service lapses and improve accountability.
● Concierge Bot – Helps customers discover and book services through natural language.
● AI-Powered Help Center – Triages and resolves customer/provider issues faster using intent classification.
Our AI-first approach is powering better decisions, faster resolution, and higher service quality across the board.
About the Data Platform Team
The Data Platform team builds and scales the intelligence layer of Urban Company. Our mission is to power data-driven decision-making, machine learning, and Gen AI across all products, geographies, and teams through robust platforms and reusable infrastructure.
We currently own and operate:
Our in-house ML Ops platform enables the entire data science lifecycle from exploratory data analysis (EDA) to training, inferencing, and parallel computation. Itʼs built for rapid experimentation and iteration, allowing teams to go from idea to production quickly.
A state-of-the-art internal platform that treats prompts as code. It offers a simplified contract driven interface with built-in monitoring and alerting on both performance and cost. With a native ORM for Vector DBs, it powers knowledge bases (KBs), Retrieval-Augmented Generation (RAG) pipelines, and model orchestration—making GenAI use case deployment seamless and scalable.
We process and analyze high-velocity data to support use cases like fraud detection, dynamic pricing, slot availability, and in-session personalization. Our frameworks are optimized for both real-time and batch workloads.
Supports rapid A/B testing and rollout strategies across product surfaces, helping teams iterate based on measurable impact.
Includes robust access control, UPSI compliance, intelligent caching, and custom in-house orchestration tools to ensure secure and efficient data workflows.
Design and scale real-time and batch computation systems using Spark. These frameworks support key use cases such as fraud detection, dynamic pricing, recommendation engines, and service availability.
Democratize data access with powerful discovery, lineage, and documentation tools. This includes building graphical data lineage, ownership mapping, and search capabilities across thousands of tables and metrics.
This team works at the intersection of infrastructure, data, and intelligence enabling every business and engineering function at UC to build smarter, faster, and safer.
Responsibilities
● Design and build backend systems that support scalable data processing and ML deployment pipelines.
● Own systems end-to-end: from high-level architecture to low-level implementation and monitoring.
● Work closely with data scientists and product teams to generalize and productionize ML workflows.
● Drive excellence in data engineering, standardization, and platform reliability.
● Stay up to date with emerging trends in data infrastructure and bring relevant innovations into the system.
● Databases: MySQL, MongoDB, Elastic Search, Pinot, Snowflake, Prometheus
● 2-4 years of backend or data engineering experience with solid computer science fundamentals.
● Strong system design skills (HLD and LLD), especially in microservice and data architecture.
● Experience working with SQL and NoSQL databases like MySQL, MongoDB.
● Familiarity with event streaming technologies (Kafka, RabbitMQ) and big data processing tools (Spark, MapReduce) is a plus.
● Passion for data systems, machine learning platforms, and enabling high-quality insights at scale.
Why Join Us
● Opportunity to solve core data platform problems at scale with real-world impact.
● Work at the intersection of engineering and data science with high visibility.
● Fast-paced, ownership-driven culture with rapid learning and career growth.
- Seniority levelMid-Senior level
- Employment typeFull-time
- IndustriesTechnology, Information and Internet
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