001
RAG ingestion & retrieval pipeline
Ingest documents, transform them, structure metadata, and keep retrieval systems current and trustworthy.
Role overview
What an Eventum Data Engineer does
At Eventum, Data Engineers build the data foundation behind our clients' AI systems: ingestion pipelines, ETL, warehouses, event models, retrieval data flows, and the operational plumbing that makes model and application layers usable.
This is the role we hire for when your bottleneck is not the model itself, but the quality, movement, structure, or availability of the data the system depends on.
Typical use cases
Typical use cases002
ML / AI data pipelines
Build the batch and streaming flows that feed training, evaluation, analytics, and production inference.
003
Event model & warehouse design
Create the schemas, transformations, and orchestration needed to support product intelligence and downstream ML systems.
004
AI application data integration
Connect data sources, APIs, internal systems, and operational signals into a usable data layer for AI products.
Key skills
ETL / ELT and pipeline design
Warehouse and event-model architecture
Batch and streaming workflows
Retrieval-friendly data preparation
Data quality, lineage, and maintainability
Orchestration frameworks and scheduling
SQL + Python + production data tooling
Integration thinking across systems, APIs, and downstream ML requirements
Testimonials
Trusted by teams who ship AI to production.
Founders, CTOs and product leads on what changed after Eventum matched them with the right AI specialist.
“Eventum matched us with an LLM engineer in six days. Two sprints later our support copilot was live in production.”
Maya LindqvistVP of Engineering, Nordflow“The vetting is real. The first candidate we interviewed was the one we hired — and she rebuilt our RAG pipeline in a month.”
Daniel WeberCTO, Parcelbase“We didn't need a whole team, we needed one senior ML engineer who could own the problem end to end. That's exactly who we got.”
Sofia MarchettiHead of Product, Klarvo“Embedded talent that actually feels in-house. Daily standups, our tooling, our codebase — zero agency friction.”
James WhitfieldFounder & CEO, Cadenza Health“Their engineer cut our inference costs by 40% in the first six weeks. The engagement paid for itself before it ended.”
Clara JensenDirector of Data, Loopwise“From brief to signed contract in under two weeks. The shortlist had three candidates, and honestly all three were hireable.”
Marcus ReinholtChief Product Officer, Ferrostack“Our computer-vision backlog was stuck for months. Eventum's specialist unblocked it in the first sprint and mentored the team along the way.”
Viktor HaldenEngineering Manager, Brightlane
Why hire through Eventum
Why hire through Eventum
01
Built for AI-adjacent data work
These are not generic analytics hires. We focus on data engineers who can support AI products, ML systems, and retrieval-heavy architectures.
02
Strong backend and infra overlap
The best candidates can move across data, services, and operational systems when needed.
03
High leverage for AI teams
A great data engineer often removes the bottleneck that keeps the whole AI roadmap from moving.
Oleg H.
Senior Data Engineer · 9 yrsBuilt AI-ready data pipelines for retrieval, analytics, and ML workflows. Experienced with ingestion, orchestration, warehouses, data quality, vector stores, and production data dependencies.
dbtAirflowSnowflakePostgresPinecone
Results
Rebuilt document-ingestion pipelines that improved retrieval freshness from weekly to near real-time.
Previously Worked at:
Michael R.
Data Platform Engineer · 11 yrsLed data infrastructure for ML, analytics, and operational workflows at growth-stage companies. Strong in ETL, streaming, warehousing, governance, feature pipelines, and data reliability.
KafkaSparkBigQueryDagsterTerraform
Results
Reduced pipeline failures by 63% after implementing data-quality checks and orchestration improvements.
Previously Worked at:
The client didn't need “AI talent” — they needed engineers who could actually deliver the data layer.
A growing AI systems consultancy needed senior engineers who could handle backend systems, data pipelines, production infrastructure, and client-facing execution in a fast-moving delivery environment. Eventum focused on engineering maturity rather than narrow specialization and delivered a strong shortlist of high-signal candidates. The client hired two senior engineers, both of whom strengthened delivery capacity across backend-, data-, and infrastructure-heavy work.
- Two senior engineering hires through Eventum
- Strong fit across backend, data, and infrastructure requirements
- Immediate increase in delivery capacity for AI systems projects
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