About the role
About the role
This role is a strong fit for data engineers with 2 to 4 years of experience who want to take their technical depth into a customer-facing role. If you have spent the last few years building pipelines, working with cloud warehouses, and solving real data problems, but you are starting to wonder what comes next, this is worth a closer look.
In this role, you will work directly with Heads of Data, Data Engineering Managers, and CTOs at companies evaluating Hevo across the US, Europe, Southeast Asia, and ANZ. You will help them design the right data architecture, walk them through trade-offs, and earn the technical trust that turns an evaluation into a partnership. You will do this independently, often as the sole technical person on the call. There is no senior SE in the background. You need to walk into a Zoom with a VP of Data Engineering at a US fintech or a Head of Analytics at a European e-commerce company, earn their technical respect in the first five minutes, and hold it for the rest of the engagement.
The technical depth you have built does not go away. It gets applied to a much wider set of problems, a much larger audience, and a global stage.
Hevo runs real-time data pipelines from 150+ sources into modern warehouses for over 1,000 companies, including DoorDash and Shopify. You will be the person customers rely on to make sure Hevo fits their stack, their scale, and their goals.
Why this role is worth considering
A. You move from building pipelines to shaping data strategy
In your current role, you build what is specified. In this role, you sit with senior data leaders and help them decide what to build across the modern data stack. Your existing experience in CDC, warehouse design, and pipeline reliability becomes the foundation for higher-leverage work.
B. You get global exposure that is hard to find elsewhere
You will be in regular technical conversations with Heads of Data and CTOs at fast-growing companies across North America, Europe, and beyond. You will run discovery, defend architecture choices, and present in rooms where decisions get made. This is rare exposure for someone at the 2 to 4 year mark, and almost unheard of from Bangalore.
C. Your technical depth stays central
This is not a pivot away from engineering. Everything you know about Postgres, CDC, Kafka, dbt, Snowflake, and pipeline reliability gets used every day. The difference is that your technical opinion shapes the customer’s architecture instead of staying inside a Jira ticket.
D. The career path is real
Strong performers in this role grow into senior individual contributor roles, lead the function, or move into product, customer success leadership, or founding-engineer roles at their next company. The optionality this role builds is significant.
What you will do
Run technical discovery calls with Heads of Data, Data Engineering Managers, and CTOs to understand their stack, pain points, and constraints. These are global conversations, primarily with customers in North America and Europe, and you will own them end to end.
Design and present reference architectures showing how Hevo fits into their existing data stack (e.g., Salesforce, Postgres, GA4, Snowflake, dbt).
Run live product demos tailored to the customer’s stack. These are not scripted walkthroughs. You will need to read the room, adapt on the fly, and handle unexpected technical questions with confidence.
Own the technical evaluation from first conversation to closed deal by addressing deep technical questions and earning trust with engineering teams.
Build proofs-of-concept by configuring real pipelines from source systems (Salesforce, Outreach, Postgres, MySQL) into destinations like Snowflake or BigQuery.
Develop deep expertise in the data integration landscape, including native solutions from AWS, GCP, and Azure (AWS Glue, Azure Data Factory, Google Cloud Dataflow) as well as other data pipeline and integration platforms, and articulate Hevo’s technical advantages in a way that lands with both technical and non-technical buyers.
Partner with Account Executives to drive technical wins, and with Customer Success to ensure smooth handoffs after a deal closes.
Share customer feedback and patterns with Product and Engineering to influence the roadmap.
What we are looking for
Must-haves
2 to 4 years of hands-on experience as a data engineer, analytics engineer, ETL/ELT developer, or data warehouse engineer.
Experience building and operating real data pipelines in production, including handling source schema changes, failures, and reliability issues.
Strong fundamentals in SQL and at least one cloud data warehouse (Snowflake, BigQuery, or Redshift).
Experience with at least one orchestration or transformation tool (Airflow, dbt, or similar).
Exceptional communication and presentation skills. This is non-negotiable. You will be the face of Hevo’s technical capability to a global customer base. That means:
Your spoken English is clear, confident, and easy to follow for an international audience. You are comfortable presenting to groups of 5 to 15 people, including senior technical leaders, over video calls. You can structure a technical narrative, not just answer questions but guide a conversation toward the right conclusion. You can hold your own when a customer pushes back or challenges your recommendation. You have presented or demo’d something technical to an audience before, in any context: internal reviews, client calls, meetups, or conferences.
Genuine interest in customer-facing work, even if you have not done it formally before.
Good to have
Experience with CDC tools or data integration platforms, whether cloud-native (AWS Glue, Azure Data Factory, Google Cloud Dataflow) or third-party pipeline and integration tools.
Exposure to multiple source system types such as CRMs, ad platforms, SaaS APIs, and OLTP databases.
Public technical writing, conference talks, or open-source contributions.
Prior experience in customer conversations, even informally. For example, presenting to internal stakeholders, analytics teams, or working with international teams across time zones.