{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “Forward Deployed Engineer (Clarasight)”, “description”: “
Requirements
- You have 5–8+ years in software engineering, data engineering, or technical consulting, with a track record of delivering in ambiguous, high-ownership environments
- You contribute to production codebases without hesitation, and you've spent meaningful time wrangling messy enterprise data. You communicate clearly across audiences, move fast, and don't need your name on things – you need them to work
- Experience with fintech or enterprise financial systems is a plus. Demonstrated outcomes applying AI/ML to real operational data is a strong signal
What the job involves
- We're looking for a Forward Deployed Engineer who thrives at the intersection of deep technical craft and high-stakes customer engagement
- This isn't a solutions engineering role where you demo software and hand off tickets
- You’ll be embedded in complex enterprise environments, building real things during live sales cycles and turning what you learn into durable product capabilities
- You’ll sit within Engineering but spend most of your time in pre-sales and strategic customer engagements, working closely with Sales and Customer Success
- Lead technical discovery with prospects and customers
- Architect and prototype integrations and AI workflows during live sales cycles
- Reconcile messy, low-quality enterprise datasets
- Contribute directly to Clarasight’s full stack production codebase
- Turn customer‑specific solutions into scalable product capabilities
- Customer Engineering owns the technical relationship from first discovery call through contract close. Collaborate with the data science team to translate ambiguous, high‑pressure business problems into working prototypes and clear technical narratives that build trust and accelerate deals
- Data & Systems Design and build pipelines across fragmented travel, financial, and expense systems. Get hands‑on with messy, low‑quality enterprise data: reconciling, validating, and shaping it into something reliable. Build frameworks that last, not workarounds that don’t
- AI Application Apply AI where it genuinely creates advantage: enrichment, anomaly detection, forecasting, automation. Have strong opinions about where it helps and where it’s noise. Turn experiments into repeatable, scalable workflows
- Productization Feed structured learnings directly into Product. The best customer‑specific solutions you build shouldn’t stay custom. Your job is to make them part of the platform
Company: Deepstreamtech
Location: London
Job Description:
Requirements
- You have 5–8+ years in software engineering, data engineering, or technical consulting, with a track record of delivering in ambiguous, high-ownership environments
- You contribute to production codebases without hesitation, and you’ve spent meaningful time wrangling messy enterprise data. You communicate clearly across audiences, move fast, and don’t need your name on things – you need them to work
- Experience with fintech or enterprise financial systems is a plus. Demonstrated outcomes applying AI/ML to real operational data is a strong signal
What the job involves
- We’re looking for a Forward Deployed Engineer who thrives at the intersection of deep technical craft and high-stakes customer engagement
- This isn’t a solutions engineering role where you demo software and hand off tickets
- You’ll be embedded in complex enterprise environments, building real things during live sales cycles and turning what you learn into durable product capabilities
- You’ll sit within Engineering but spend most of your time in pre-sales and strategic customer engagements, working closely with Sales and Customer Success
- Lead technical discovery with prospects and customers
- Architect and prototype integrations and AI workflows during live sales cycles
- Reconcile messy, low-quality enterprise datasets
- Contribute directly to Clarasight’s full stack production codebase
- Turn customer‑specific solutions into scalable product capabilities
- Customer Engineering owns the technical relationship from first discovery call through contract close. Collaborate with the data science team to translate ambiguous, high‑pressure business problems into working prototypes and clear technical narratives that build trust and accelerate deals
- Data & Systems Design and build pipelines across fragmented travel, financial, and expense systems. Get hands‑on with messy, low‑quality enterprise data: reconciling, validating, and shaping it into something reliable. Build frameworks that last, not workarounds that don’t
- AI Application Apply AI where it genuinely creates advantage: enrichment, anomaly detection, forecasting, automation. Have strong opinions about where it helps and where it’s noise. Turn experiments into repeatable, scalable workflows
- Productization Feed structured learnings directly into Product. The best customer‑specific solutions you build shouldn’t stay custom. Your job is to make them part of the platform
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Posted: May 18th, 2026