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. 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. The technical depth you have built does not go away. It gets applied to a much wider set of problems and a much larger audience.

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 :

1. 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.

2. You get exposure that is hard to find elsewhere

- You will be in regular technical conversations with Heads of Data and CTOs at fast-growing companies. 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.

3. 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 customers architecture instead of staying inside a Jira ticket.

4. 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 :

1. Run technical discovery calls with Heads of Data, Data Engineering Managers, and CTOs to understand their stack, pain points, and constraints.

2. Design and present reference architectures showing how Hevo fits into their existing data stack (e.g., Salesforce, Postgres, GA4, Snowflake, dbt).

3. Own the technical evaluation end-to-end, from first conversation to closed deal, by addressing deep technical questions and earning trust with engineering teams.

4. Build proofs-of-concept by configuring real pipelines from source systems (Salesforce, Outreach, Postgres, MySQL) into destinations like Snowflake or BigQuery.

5. Partner with Account Executives to drive technical wins, and with Customer Success to ensure smooth handoffs after a deal closes.

6. Share customer feedback and patterns with Product and Engineering to influence the roadmap.

What we are looking for :

Must-haves :

1. 2 to 4 years of hands-on experience as a data engineer, analytics engineer, ETL/ELT developer, or data warehouse engineer.

2. Experience building and operating real data pipelines in production, including handling source schema changes, failures, and reliability issues.

3. Strong fundamentals in SQL and at least one cloud data warehouse (Snowflake, BigQuery, or Redshift).

4. Experience with at least one orchestration or transformation tool (Airflow, dbt, or similar).

5. Ability to explain a complex technical decision clearly to both technical and non-technical audiences.

5. Genuine interest in customer-facing work, even if you have not done it formally before.

Good to have :

1. Experience with ETL tools (Debezium, Fivetran, Airbyte, Hevo) or having built CDC systems.

2. Exposure to multiple source system types such as CRMs, ad platforms, SaaS APIs, and OLTP databases.

3. Public technical writing, conference talks, or open-source contributions.

4. Prior experience in customer conversations, even informally. For example, presenting to internal stakeholders or analytics teams.