Customer Success Engineer

About Hevo

(HevoData.com)Doordash, Footlocker, Arhaus, Santander, and thousands of other data-driven companies have one thing in common. They all use Hevo Data's fully managed Automated Pipelines to consolidate their data from multiple sources like Databases, Marketing Applications, Cloud Storage, SDKs, Streaming Services, etc.

We are a San Francisco/Bangalore-based company with 2000+ customers spread across 45+ countries in e-commerce, financial technology, and healthcare. Strongly backed by marquee investors like Sequoia Capital ,we have raised $42 Million to date and are looking forward to our next phase of hyper-growth!

Why do we ExistAt Hevo, our mission is to enable every company to be data-driven. We started on this journey 4 years back and as the first step in this direction, we built our first product Data Pipeline or simply Pipeline.

Hevo Pipeline is a no-code platform that helps companies to connect all their sources of data within the company to get a unified view of their business. The platform offers integrations with 150+ data sources such as Databases, SaaS applications, Advertising Channels, etc.

Today, we enable close to 2000 companies across more than 40+ countries to be more data-driven. Our aim is to make the technology so simple that anyone should be able to solve their data problems and not be limited due to their lack of technical skills.

The role : Customer Success Engineer

ally. This role reports to the Head of Customer Success. Ideal candidates will have expertise in data warehousing, data pipelines, and ETL/ELT to be consultative experts making strategic and consultative recommendations regarding all Hevo-related technical matters.

What will you do

? Manage a set of customers (book of business) and own the retention of the assigned customers

? Engage with customers, in a technical consultancy and advisor role during the post-sales process while providing technical and solution guidance

? Customer Onboarding- Develop Customer onboarding for all new customers assigned to you.

? Build and develop data pipelines

? Build out of the box solutions when we hit product limitations

? Be the Single point of contact for all technical matters.

? Drive Engagement and Adoption. work with Account teams to communicate ROI.

? Lead and Organize Quarterly Business reviews.

? Drive mindshare within the customer team.

? Identify new use cases for Hevo products within the customer base.

? Drive Adoption of new features and products

? Ensure that the solution promised to the customer will work to achieve the goals and challenges they brought.

? Maintain specialty competency in one or more technologies related to Hevo's market focus through activities such as training, certification, and creation of working examples for reuse internally and by customers and partners

? Inform and train clients on the proper use of the product to enhance the customer experience and improve satisfaction.

? Act as an advocate for customers during the triage and coordinate with respective Hevo teams to resolve high-severity cases and assist with the timely resolution of these issues.

What are we looking for

? Understanding of the Database Administration/ Data Replication domain.

? 2-5 years of experience in a relative field such as ETL/ELT, Data transfer/Data ware housing

? Understand data integration principles, connecting diverse data sources to a warehouse. Possess in-depth knowledge of extracting data from multiple specific sources.

? Proficiency in SQL is vital for querying and manipulating data.

? Cloud platform familiarity (e.g. AWS, GCP, Azure) is essential, as Hevo often pairs with them.

? Knowledge of databases like PostgreSQL, MySQL, SQL Server, and Oracle is crucial.

? Experience with API integration and JSON/XML data formats is helpful.

? Basic proficiency in data transformation tools/languages like Python, or warehouse-specific tools is valuable for data cleaning and enrichment.

? Should have a knack for problem-solving and debugging.

? Understanding in networking, security, and performance concepts is required.

? Comprehend data quality best practices for accurate, reliable integrated data.

? Understand data warehousing concepts, including modeling, schemas (e.g., star, snowflake), normalization/denormalization, and optimization.