Marketing Platform Engineer (Data & AI)

Bloomreach is building the world’s premier agentic platform for personalization.We’re revolutionizing how businesses connect with their customers, building and deploying AI agents to personalize the entire customer journey.

We're taking autonomous search mainstream, making product discovery more intuitive and conversational for customers, and more profitable for businesses.

We’re making conversational shopping a reality, connecting every shopper with tailored guidance and product expertise — available on demand, at every touchpoint in their journey.

We're designing the future of autonomous marketing, taking the work out of workflows, and reclaiming the creative, strategic, and customer-first work marketers were always meant to do.

And we're building all of that on the intelligence of a single AI engine — Loomi AI — so that personalization isn't only autonomous…it's also consistent.From retail to financial services, hospitality to gaming, businesses use Bloomreach to drive higher growth and lasting loyalty. We power personalization for more than 1,400 global brands, including American Eagle, Sonepar, and Pandora.

Bloomreach is seeking a Context Layer & Platform Engineer to architect and maintain the intelligent data backbone powering our GTM AI ecosystem. In this role, you will work hand-in-hand with the AI Ops team, Sales, and Marketing to design and scale a centralized, real-time context retrieval system that serves 50-200 GTM agents with precision, speed, and minimal noise. You'll engineer the architecture so that agents call clean, versioned endpoints to access the right data at the right time, in the right channel, at the right freshness - without redundant work or data drift. You'll own Salesforce data quality, enrichment, and deduplication while simultaneously building the infrastructure that makes our agents smarter, faster, and more reliable. The ideal candidate combines deep technical expertise in data pipelines and API architecture with product thinking about how data flows through systems, thrives solving complex orchestration challenges, and is motivated to turn messy data into intelligence at scale.

Your salary starts from 4000 € gross per month with restricted stock units and other benefits included. You can work from our Bratislava office or from home (Slovakia).

Your job will be to:

Own Salesforce data integrity: Take responsibility for data quality, deduplication, and enrichment across Salesforce. Use AI enriching accounts and contacts with the right data, eliminating duplicates, standardizing fields, validating data freshness, and ensuring that all context flowing through the system is reliable. You are the keeper of the single source of truth.

Engineer the context retrieval backbone: Design and ship versioned, documented API endpoints that GTM agents call to retrieve context - contacts, accounts, engagement history, intent signals, enrichment data - with consistent response contracts, fast latency, and intelligent filtering/boosting/time-decay logic. These endpoints become the foundation for scaling from 10 to 200 agents.

Optimize retrieval performance: Continuously tune query logic, filters, boosts, and ranking algorithms so answers are contextually relevant, fast (sub-second latency), and cost-effective. Every agent query should return signal, not noise.

Scale “GTM Brain” data sources without breaking agents: Add new sources - product usage, web signals, third-party enrichment, intent platforms - to the context layer without forcing agents to rebuild their logic. Design the system so agents stay stable while context grows richer.

Keep context fresh and resilient: Build and operate background jobs that refresh context on schedule, detect and fix failed re-embeddings, backfill deltas, and maintain data pipelines that feed agents with up-to-date intelligence without disruption or drift.

Enable agent developers and operators: Create starter queries and templated context patterns that new agents can adopt quickly. Coordinate with the enablement and AI Ops teams to make context accessible to non-engineers.

Collaborate on vendor strategy: Help evaluate, integrate, and own relationships with key vendors like N8N, Clay, Rattle - ensuring their data flows cleanly into your context architecture and meets our latency and freshness requirements.

Participate in building the GTM data stack of the future: Work with RevOps, and AI Ops to understand how Salesforce, enrichment platforms, N8N, Supabase/Postgres, LLM tools, and analytics integrate - co-creating a scalable, maintainable system that can grow to support 200+ agents without technical debt.

Your success story will be:

Within the first 30-60 days: The first set of centralized context endpoints are shipped, versioned, and documented. At least 3 existing agents are migrated from custom context logic to calling these endpoints. Salesforce data quality audit is complete, enrichment roadmap is clear, and foundational data cleanup is underway.

After 60-90 days: GTM Brain architecture designed, Clear context layer roadmap to BigQuery, SFDC and others defined and in works.

By 6 months: 15-20 agents are operating on the platform connected to GTM Brain. Salesforce data quality has improved by 40%+ (measured by duplicate rates, field completeness, freshness). A new data source (usage signals, enrichment vendor, etc.) has been integrated without disrupting existing agents. The context layer is stable and predictable enough that the AI Ops team confidently plans to scale to 50+ agents.

You have the following experience and qualities:

You've built or scaled data pipelines, ETL systems, or API backends that serve multiple downstream consumers - and understand the discipline of versioning, backward compatibility, and operational stability.

You know Salesforce deeply - data model, custom objects, field governance, deduplication strategies, API limitations - and can architect data quality processes that scale.

You're expert with N8N, Supabase/Postgres, and API integrations. You can design schemas, optimize queries, version APIs, and think about performance at scale. You've built systems that balance flexibility with simplicity.

You understand LLM-adjacent tooling and can reason about prompt versioning, retrieval quality, latency trade-offs, and how to structure data so agents consume it predictably.

You love operational rigor - monitoring, alerting, observability, runbooks. You believe that boring infrastructure is good infrastructure, and you want to measure and communicate it.

You're comfortable with ambiguity and can break down a big architectural challenge ("serve 200 agents with consistent context") into testable, shippable milestones.

You thrive in cross-functional environments and can translate between data engineers, product teams, AI researchers, and business stakeholders. You can speak both technical and business language.

You're driven by the challenge of turning messy data into clean intelligence - and seeing that intelligence power products and decisions in real time.

You're curious about how GTM works and motivated to understand not just the "what" but the "why" - so you build systems that are useful, not just technically sound.

You have strong project ownership instincts - you see things through from design to maintenance, and you take pride in systems that run reliably at scale.