Staff Engineer - Recommendations

Jobgether; India; On-site; INR 1,500,000 - 2,300,000; Full time

Benefits offered by this job

Health benefits

Stock options

Flexible vacations

Job summary

Jobgether is seeking a Staff Engineer - Recommendations based in India to build the data and machine learning foundations powering personalized discovery at scale. You will design systems handling large volumes of platform-generated data across data eng, backend, and personalization teams.

You will collaborate with data scientists and engineers to turn behavioral signals into meaningful user experiences, advancing from heuristics to data-driven models while ensuring scalable, observable systems

Qualifications

• 3+ years of software engineering experience focusing on data engineering/backend systems.
• Proven experience designing and optimizing production-grade ETL/ELT pipelines.
• Strong SQL and experience with big data tech (Spark, Kafka, Hadoop, Beam).

Responsibilities

• Design, develop, maintain, and optimize scalable data pipelines and APIs for recommendations and content discovery.
• Build data models and schemas supporting analytical workloads and real-time personalization.
• Collaborate with data scientists, PMs, and engineers to capture and process user/platform data.
• Develop and maintain large-scale data processing workflows with Spark and Kafka.
• Contribute to backend architecture including REST/WebSocket APIs and cloud orchestration.

Skills

Data engineering

Backend systems

Distributed systems

SQL

Python/Node.js

Tools

Spark

Kafka

Hadoop

Beam

Elasticsearch

MongoDB

Redis

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Engineer - Recommendations based in India.

This role focuses on building the data and machine learning foundations that power personalized discovery and social experiences at scale.
You will help evolve recommendation systems that connect users with relevant content, communities, groups, and events.
Working across data engineering, backend development, and personalization, you will design systems capable of processing large volumes of platform-generated data.
You will collaborate closely with data scientists, product managers, and engineers to turn behavioral signals into meaningful user experiences.
The role offers significant technical scope across cloud infrastructure, data pipelines, APIs, distributed systems, and ML-enabled applications.
You will also help advance personalization from simple heuristics toward increasingly sophisticated, data-driven recommendations.
This is an opportunity to make a direct impact on a highly interactive consumer platform within a distributed, collaborative engineering environment.

Accountabilities

• Design, develop, maintain, and optimize scalable data pipelines, backend services, and APIs supporting recommendations, content discovery, groups, events, and other data-driven experiences.
• Build data models and schemas that support both analytical workloads and real-time personalization and recommendation systems.
• Partner with data scientists, product managers, and engineering teams to ensure relevant user and platform data is accurately captured, processed, and made available for product experiences.
• Develop and maintain large-scale data processing workflows using technologies such as Spark and Kafka.
• Help evolve recommendation capabilities from basic heuristics toward more sophisticated, data-backed personalization models.
• Contribute to backend architecture and implementation, including REST and WebSocket APIs, caching systems, queueing infrastructure, and cloud orchestration.
• Process and transform high-volume platform data into reliable datasets and signals that can support machine learning and personalization use cases.
• Optimize data storage, processing, and database performance for both analytical workloads and high-throughput real-time applications.
• Collaborate across a full-stack engineering environment to deliver reliable, scalable features from data layer through user-facing experiences.
• Contribute to technical strategy and the evolution of engineering and product capabilities as recommendation and personalization needs grow.
• Monitor production systems and participate in incident response, including occasionally supporting urgent troubleshooting during outages.
• Promote strong engineering practices around scalability, reliability, maintainability, observability, and data quality.

Requirements

• 3+ years of professional software engineering experience, with a strong focus on data engineering, backend systems, or scalable SaaS and online platforms.
• Proven experience designing, building, and optimizing production-grade ETL/ELT data pipelines.
• Strong SQL skills, including database optimization for analytical workloads and high-throughput real-time access.
• Hands-on experience with big data technologies such as Spark, Kafka, Hadoop, or Beam.
• Experience working with cloud platforms at scale, particularly AWS or Google Cloud.
• Programming experience across technologies such as Python, JavaScript/Node.js, MongoDB, and Redis, with the ability to work effectively across multiple languages and systems.
• Experience with Elasticsearch, data warehousing, and machine learning systems.
• Strong understanding of scalable backend architecture, distributed data processing, APIs, caching, queues, and cloud infrastructure.
• Ability to collaborate effectively with data scientists, product managers, engineers, and other cross-functional stakeholders.
• Strong communication skills and an agile, collaborative mindset suited to a distributed engineering environment.
• Bonus experience with content discovery, recommendation engines, personalization, social graphs, online communities, or user-generated content.
• Experience building consumer products, e-commerce platforms, marketplaces, or social products is highly valued.
• Interest or experience in virtual reality, online communities, or creator-driven platforms is a plus.

Benefits

• 100% remote work with flexible working hours and designated core collaboration hours.
• Health benefits.
• 401(k) plan for eligible U.S. employees.
• Stock options.
• Generous paid holiday schedule.
• Unlimited and flexible vacation time.
• Paid parental leave.
• Opportunity to work on large-scale recommendation, data, and personalization systems.
• Collaborative and distributed environment where engineers can contribute to projects and influence technical direction.