Data Engineer SE - II

We are on a mission to rid the world of bad customer service by “mobilizing” the way help is delivered. Today’s consumers want an always-available customer service experience that leaves them feeling valued and respected. Helpshift helps B2B brands deliver this modern customer service experience through a mobile-first approach. We have changed how conversations take place, moving the conversation away from a slow, outdated email and desktop experience to an in-app chat experience that allows users to interact with brands in their own time. Through our market-leading AI-powered chatbots and automation, we help brands deliver instant and rapid resolutions. Because agents play a key role in delivering help, our platform gives agents superpowers with automation and AI that simply works. Companies such as Scopely, Supercell, Brex, EA, Square along with hundreds of other leading brands use the Helpshift platform to mobilize customer service delivery. Over 900 million active monthly consumers are enabled on 2B+ devices worldwide with Helpshift.

Some numbers that illustrate our scale:

85k/rps

30ms response time

300 GB data transfer/hour

1000 VMs deployed at peak

About the team -

Consumers care first and foremost about having their time valued by brands. Brands need insights into their customer service operation to serve their consumers effectively. Such insights and analytics are delivered through various data products like in-app analytics dashboards and data-sharing integrations. 

The data platform team is responsible for designing, building, and maintaining the data infrastructure that enables such data and analytics products at scale. We build and manage data pipelines, databases, and other data structures to ensure that the data is reliable, accurate, and easily accessible. We also enable internal stakeholders with business intelligence and machine learning teams with data ops. This team manages the platform that handles 2 Million events per minute and processes 1+ terabytes of data daily.

Requirements

• 4+ years of experience in backend engineering, building scalable, reliable, and high-performance systems in production environments
• Strong proficiency in at least one high-level programming language (Python, Java, or similar) with solid grounding in data structures, algorithms, and problem-solving
• Strong working knowledge of SQL, including query optimization, performance tuning, and working with large-scale datasets
• Hands-on experience with relational and/or analytical databases (e.g., Snowflake, Redshift, PostgreSQL, MySQL, etc.), including data modeling and schema design
• Good understanding of distributed systems fundamentals, including scalability, fault tolerance, and system design
• Familiarity with cloud platforms (AWS/GCP/Azure) and modern data infrastructure concepts
• Exposure to data-intensive applications, such as high-throughput APIs, event-driven systems, or analytics platforms
• Interest in or experience with modern data platforms (e.g., Snowflake) and willingness to quickly learn data engineering tooling and paradigms
• Basic understanding of security, networking, and production system operations (monitoring, observability, reliability)
• Exposure to or strong interest in GenAI/LLM capabilities (e.g., embeddings, vector search, prompt engineering, or LLM-powered applications) is highly desirable
• Experience with streaming or real-time systems (Kafka, Kinesis, etc.) is a plus
• Experience with data pipeline technologies (Spark, Airflow, etc.) is a plus
• Data visualization skills are a plus (PowerBI, Metabase, Tableau, Hex, Sigma, etc)
• Bachelor’s Degree in Computer Science (or equivalent)
• Strong verbal and written communication skills

Responsibilities

• Design and build scalable backend systems that power data-intensive and analytics-driven use cases
• Develop high-performance data access layers and services, enabling efficient querying, transformation, and serving of large datasets
• Work with modern platforms like Snowflake to build and optimize data workflows
• Build and contribute to intelligent data products, leveraging GenAI/LLM capabilities for use cases like anomaly detection, summarization, and insights generation
• Apply strong data modeling practices to design clean, scalable, and maintainable data schemas for analytics and product use cases
• Collaborate closely with product, analytics, and business stakeholders to translate requirements into robust technical solutions
• Own end-to-end system design including architecture, performance, scalability, reliability, and security considerations
• Continuously improve system performance through query optimization, caching strategies, and efficient data access patterns
• Contribute to real-time and near real-time data processing systems where required
• Write clear design documents, conduct code reviews, and ensure high engineering standards
• Mentor team members on backend engineering best practices, system design, and modern data platform usage

Benefits

• Hybrid setup
• Worker's insurance
• Paid Time Offs
• Other employee benefits to be discussed by our Talent Acquisition team in India.

Helpshift embraces diversity. We are proud to be an equal opportunity workplace and do not discriminate on the basis of sex, race, color, age, sexual orientation, gender identity, religion, national origin, citizenship, marital status, veteran status, or disability status

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- 4+ years of experience in backend engineering, building scalable, reliable, and high-performance systems in production environments - Strong proficiency in at least one high-level programming language (Python, Java, or similar) with solid grounding in data structures, algorithms, and problem-solving - Strong working knowledge of SQL, including query optimization, performance tuning, and working with large-scale datasets - Hands-on experience with relational and/or analytical databases (e.g., Snowflake, Redshift, PostgreSQL, MySQL, etc.), including data modeling and schema design - Good understanding of distributed systems fundamentals, including scalability, fault tolerance, and system design - Familiarity with cloud platforms (AWS/GCP/Azure) and modern data infrastructure concepts - Exposure to data-intensive applications, such as high-throughput APIs, event-driven systems, or analytics platforms - Interest in or experience with modern data platforms (e.g., Snowflake) and willingness to quickly learn data engineering tooling and paradigms - Basic understanding of security, networking, and production system operations (monitoring, observability, reliability) - Exposure to or strong interest in GenAI/LLM capabilities (e.g., embeddings, vector search, prompt engineering, or LLM-powered applications) is highly desirable - Experience with streaming or real-time systems (Kafka, Kinesis, etc.) is a plus - Experience with data pipeline technologies (Spark, Airflow, etc.) is a plus - Data visualization skills are a plus (PowerBI, Metabase, Tableau, Hex, Sigma, etc) - Bachelor’s Degree in Computer Science (or equivalent) - Strong verbal and written communication skills