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 We have changed how conversations take place, moving the conversation away from a slow, outdated email and desktop experience to an in-app chat experience that allows users to interact with brands in their own time.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

Requirements

Requirements
Experience: 5+ years of Data Engineering experience, with a proven track record of building complex, high-performance data pipelines and AI-driven systems in production environments.
Deep Snowflake Expertise: Extensive, hands-on professional experience with Snowflake, including advanced data modeling, schema design, and native Snowflake features.
Programming Mastery: Expert-level proficiency in Python and SQL, with strong foundational knowledge in data structures, algorithms, and software engineering best practices.
Snowflake Cortex & GenAI: Demonstrable experience leveraging Snowflake Cortex AI to build scalable GenAI and Agentic use-cases directly within the data platform.
Advanced Data Pipelines: Extensive experience designing and implementing complex, automated data pipelines utilizing Snowflake Stored Procedures (Snowpark Python/SQL).
LLM Cost & Performance Optimization: Deep understanding of performance tuning and cost optimization specifically tailored for LLMs, vector searches, and Agentic systems running within cloud data platforms.
Data Products via Streamlit: Hands-on experience building, deploying, and optimizing interactive data applications and user interfaces using Streamlit.
Cloud Infrastructure: Familiarity with modern cloud platforms (AWS/GCP/Azure) and their integration with Snowflake data infrastructure.
System Operations: Solid understanding of production system operations, including observability, data quality monitoring, reliability, and Snowflake security/RBAC.
Bonus Skills: Experience with modern orchestrators (e.g., Airflow, Dagster), streaming data (Kafka, Snowpipe Streaming), or traditional BI tools is a plus, but secondary to Snowpark/Cortex expertise.
Education: Bachelor’s Degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
Communication: Strong verbal and written communication skills, with the ability to translate complex AI/data concepts to business stakeholders efficiently.
Responsibilities
Cortex & Agentic Architecture: Architect and implement advanced Agentic use-cases leveraging Snowflake Cortex, embedding LLM capabilities (summarization, anomaly detection, automated insights) directly into core data workflows.
Complex Pipeline Development: Design, build, and deploy highly scalable data pipelines natively within Snowflake using Snowpark and Python/SQL Stored Procedures.
Cost & Performance Optimization: Rigorously monitor, troubleshoot, and optimize Snowflake compute costs and query performance, specifically focusing on the execution efficiency of LLMs and Agentic functions.
Streamlit Application Delivery: Rapidly prototype, build, and deploy intelligent, interactive data products and internal tools using Streamlit to surface GenAI insights to business users.
Data Modeling & Architecture: Apply expert-level data modeling practices to design clean, scalable, and secure data schemas that support both traditional analytics and complex AI/ML workloads.
Rapid Iteration & Delivery: Operate effectively in a fast-paced contract environment, owning end-to-end technical delivery from architecture to deployment, and hitting the ground running to solve immediate business challenges.
Cross-Functional Collaboration: Collaborate closely with product managers, analytics teams, and business stakeholders to translate requirements into robust, AI-powered technical solutions.
Engineering Standards: Maintain high engineering standards by writing clear technical design documents, conducting thorough code reviews, and implementing CI/CD best practices for Snowflake artifacts.

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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SE II Data Engineer (Contract) - 6 Months

Description

Experience: 5+ years of Data Engineering experience, with a proven track record of building complex, high-performance data pipelines and AI-driven systems in production environments.

Deep Snowflake Expertise: Extensive, hands-on professional experience with Snowflake, including advanced data modeling, schema design, and native Snowflake features.

Programming Mastery: Expert-level proficiency in Python and SQL, with strong foundational knowledge in data structures, algorithms, and software engineering best practices.

Snowflake Cortex & GenAI: Demonstrable experience leveraging Snowflake Cortex AI to build scalable GenAI and Agentic use-cases directly within the data platform.

Advanced Data Pipelines: Extensive experience designing and implementing complex, automated data pipelines utilizing Snowflake Stored Procedures (Snowpark Python/SQL).

LLM Cost & Performance Optimization: Deep understanding of performance tuning and cost optimization specifically tailored for LLMs, vector searches, and Agentic systems running within cloud data platforms.

Data Products via Streamlit: Hands-on experience building, deploying, and optimizing interactive data applications and user interfaces using Streamlit.

Cloud Infrastructure: Familiarity with modern cloud platforms (AWS/GCP/Azure) and their integration with Snowflake data infrastructure.

System Operations: Solid understanding of production system operations, including observability, data quality monitoring, reliability, and Snowflake security/RBAC.

Bonus Skills: Experience with modern orchestrators (e.g., Airflow, Dagster), streaming data (Kafka, Snowpipe Streaming), or traditional BI tools is a plus, but secondary to Snowpark/Cortex expertise.

Education: Bachelor’s Degree in Computer Science, Engineering, or a related field (or equivalent practical experience).

Communication: Strong verbal and written communication skills, with the ability to translate complex AI/data concepts to business stakeholders efficiently.

Cortex & Agentic Architecture: Architect and implement advanced Agentic use-cases leveraging Snowflake Cortex, embedding LLM capabilities (summarization, anomaly detection, automated insights) directly into core data workflows.

Complex Pipeline Development: Design, build, and deploy highly scalable data pipelines natively within Snowflake using Snowpark and Python/SQL Stored Procedures.

Cost & Performance Optimization: Rigorously monitor, troubleshoot, and optimize Snowflake compute costs and query performance, specifically focusing on the execution efficiency of LLMs and Agentic functions.

Streamlit Application Delivery: Rapidly prototype, build, and deploy intelligent, interactive data products and internal tools using Streamlit to surface GenAI insights to business users.

Data Modeling & Architecture: Apply expert-level data modeling practices to design clean, scalable, and secure data schemas that support both traditional analytics and complex AI/ML workloads.

Rapid Iteration & Delivery: Operate effectively in a fast-paced contract environment, owning end-to-end technical delivery from architecture to deployment, and hitting the ground running to solve immediate business challenges.

Cross-Functional Collaboration: Collaborate closely with product managers, analytics teams, and business stakeholders to translate requirements into robust, AI-powered technical solutions.

Engineering Standards: Maintain high engineering standards by writing clear technical design documents, conducting thorough code reviews, and implementing CI/CD best practices for Snowflake artifacts.

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