Sales Engineer

Overview

You're the technical expert supporting AEs in sales cycles. You run product demos, design POCs, and answer data architecture questions from prospects' data engineering and marketing ops teams. Most of your deals involve connecting to prospects' Snowflake, Databricks, or BigQuery environments and proving Hightouch can sync their specific data models to tools like Braze, Google Ads, or Salesforce.

Role Snapshot

Company Context

Stage: Series C+ (503 employees, well-funded)

Size: 503 employees

Growth: Active hiring across all functions, Forbes #8 Best Startup Employer

Market Position: Category leader in Composable CDP / Reverse ETL with 250+ integrations - you're often educating buyers on the category itself

GTM Reality

Pipeline Sources:

• You support AEs on inbound leads (50%) and outbound-sourced deals (50%)
• Most deals require your involvement because of technical complexity - AEs can't demo or run POCs without you

SDR/AE Structure: You're paired with 3-4 AEs; you join their calls once a deal reaches demo/technical evaluation stage

SE Support: You are the SE support - you're expected to handle everything from basic demos to complex multi-week POCs

Competitive Landscape

Main Competitors: Segment, Census, build-it-yourself approaches

How They Differentiate: 250+ integrations, AI decisioning, marketing-friendly UI

Common Objections: "Can't our data team build this?", "How is this different from Segment?", "Will this slow down our warehouse?"

Win Themes: Your job is proving speed to value, showing warehouse performance isn't impacted, and demonstrating marketing team can self-serve

What You'll Actually Do

Time Breakdown

Demos/POCs (50%) | Discovery/Scoping (25%) | Internal Prep (25%)

Key Activities

• Technical discovery: Before demos, you spend 30-60 mins understanding their data stack (which warehouse, what tools they use, data model structure, current pain points). You're figuring out what to show in the demo that'll resonate - syncing product catalog data? Behavioral audience segments? Custom event triggers?
• Product demos: You screenshare and walk through connecting Hightouch to a demo warehouse, building a segment or audience, and syncing it to a marketing tool. You're explaining SQL transformations, data mappings, and how syncing works without copying data. Good demos feel like magic; bad demos get bogged down in "wait, why isn't this field mapping correctly?"
• POC setup and management: About 40% of deals require a POC where you connect to their actual warehouse (with read-only access). You help them set up their first real syncs, troubleshoot data quality issues, and prove the platform works with their specific setup. POCs take 1-2 weeks and often surface issues ("our tables aren't normalized", "we don't have the fields we thought we did").
• Architecture conversations: Data engineering teams want to understand how Hightouch queries their warehouse, handles scale, manages API rate limits, and ensures security. You draw architecture diagrams, explain query optimization, and sometimes need to pull in backend engineers for deep technical questions.
• Competitive positioning: When prospects say "we can build this ourselves" or "how is this different from Segment?", you need technical answers. You show code complexity of DIY approaches, explain architectural differences, and demonstrate capabilities competitors don't have.

The Honest Reality

What's Hard

• Messy customer data: Most prospects have poorly structured warehouse data - inconsistent naming, missing fields, non-normalized tables. Your POC gets delayed because they need to clean up their data first. You spend a lot of time saying "we can work with this, but it'll be easier if you restructure these tables."
• Explaining a new category: Composable CDP / Reverse ETL isn't an obvious concept. You're teaching prospects why syncing warehouse → tools is better than traditional CDPs. Some get it immediately; others never quite understand the architectural shift.
• Technical gatekeeping: Data engineering teams are skeptical of "yet another vendor accessing our warehouse." They worry about performance, security, and cost. You need to credibly address concerns about query load, data governance, and why this isn't just a glorified ETL tool they could build.
• Long POC cycles: POCs drag because customers are busy, their data team has other priorities, or they hit unexpected technical issues. A 2-week POC turns into 6 weeks. You're constantly chasing them for the next step.

What Success Looks Like

• Supporting 8-12 active deals at once across discovery, demo, and POC stages
• 70%+ win rate on deals where you run a successful POC
• Demos that make technical buyers excited ("this is way easier than I thought")
• Handling objections from data engineers without needing to escalate to product/engineering teams

Who You're Selling To

Primary Buyers:

• Director of Marketing Operations (business buyer, cares about use cases and ROI)
• Head of Data Engineering (technical gatekeeper, cares about architecture and warehouse impact)
• IT Security (sometimes involved, cares about data governance and access controls)

What They Care About:

• Marketing Ops: Can we self-serve? How quickly can we set up new syncs? Will this actually improve our campaigns?
• Data Engineering: Will this slow down our warehouse? How much access does it need? Can we audit what data is syncing where?
• Security: Is it SOC 2 compliant? How is data encrypted? Who has access?

Requirements

• 3-5 years as a Solutions Engineer, Sales Engineer, or Data Engineer with customer-facing experience
• Strong SQL skills - you need to write queries live in demos and troubleshoot customer data models
• Experience with modern data stacks (Snowflake, Databricks, BigQuery) and understanding of data warehousing concepts
• Familiarity with marketing tools and APIs (ad platforms, email/marketing automation, CRMs)
• Ability to explain complex technical concepts to both technical and non-technical audiences
• Comfortable in ambiguous situations (POCs where you don't know what you'll find in their data)