Forward Deployed Engineering
CL Forward
Deployed Engineers.
We enable organisations to build Forward Deployed Engineering capability — shortlisting specialists who embed inside your team, own delivery outcomes, and move you from prototype to production in weeks, not quarters.
ComputeLogic Certified™
Every FDE passes our 3-stage proprietary vetting process.
Databricks certifications verified. Live architecture review scored. Client-embedded delivery readiness assessed before you see a single CV.
Only ~12% of applicants earn CL Certified status
Our Proprietary System
How We Vet: The 3-Gate Process
Every CL Certified engineer passes three scoring gates — only those who clear all three join the bench.
Technical Deep-Dive
Databricks Coding Challenge
Candidates complete a 90-minute coding challenge on Databricks.
Timed: 90-min live coding challenge on Databricks
Architecture Interview
Logic Assessment
A senior ComputeLogic principal runs a whiteboard architecture session. We assess system design thinking: how they'd model a Medallion Architecture for a specific domain, handle CDC data, and design a governance-first Unity Catalog hierarchy.
Architecture review board with a senior CL principal
Culture Sync
Deployment Readiness
The final stage. We verify client-facing communication, documentation standards, and delivery methodology alignment. You receive the full assessment scorecard before your first interview.
Culture alignment + reference check before placement
The Bench
FDEs Shortlisted and Ready to Embed in 72 Hours
Data FDE
Embedded pipeline engineers who own your Databricks data estate — not handed off after go-live
Your Databricks pipeline estate, owned end to end by someone embedded in your team. CL Data FDEs build with production in mind from day one — Photon-tuned, SDP-native, and accountable for the latency and quality numbers that show up in your dashboards.
Core Capabilities
- Spark Declarative Pipelines: Streaming Tables & Materialized Views
- Medallion Architecture: Bronze → Silver → Gold with Z-Order
- SDP Flows: incremental batch + streaming from Kafka, Kinesis & S3
- Spark optimization: broadcast joins, AQE, partition pruning
- dbt on Databricks + Unity Catalog model governance
Typical ramp time: Day 1. Databricks Certified Associates.
Platform FDE
Senior Lakehouse architects embedded for governance-first, production-ready design
Senior architects who embed to design the Unity Catalog topology, multi-workspace RBAC hierarchy, and data mesh domain boundaries before a single table is created. They eliminate the governance technical debt that accrues from bolt-on fixes.
Core Capabilities
- Unity Catalog design: metastore, catalog, schema hierarchy
- Multi-workspace and multi-cloud topology with Delta Sharing
- Row/column-level FGAC + Attribute-Based Access Control
- Data mesh ownership frameworks with clear SLA contracts
- Databricks FinOps: cluster policies, spot strategy, Photon ROI
Databricks Champion Verified. Architecture review on day 1.
AI FDE
Production AI engineers — fine-tune, deploy, and monitor on AI/ML Flows on Databricks inside your environment
End-to-end AI practitioners who fine-tune DBRX and open-source models on your proprietary data estate, deploy to Model Serving endpoints with sub-200ms P95 latency, and instrument with MLflow. No separate AI stack required.
Core Capabilities
- AI/ML fine-tuning: DBRX, LLaMA, Mistral on private data
- MLflow: experiment tracking, model registry, reproducibility
- RAG pipelines: Vector Search + embedding generation at scale
- Feature Store engineering for real-time + batch features
- Model monitoring: drift detection, A/B testing, shadow mode
Production AI deployed in under 2 weeks.
Need an FDE Embedded by Next Sprint?
Tell us the role, the stack, and the start date. We'll shortlist pre-vetted CL Certified FDE profiles — with full assessment scorecards — in your inbox within 72 hours. No recruitment theatre, no wasted interviews.
CL Certified FDEs · Scorecard included · 72h shortlist SLA