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THE COMPOUND DELIVERY MODEL

A signature system for high-trust data outcomes

Most data teams optimize for tooling and outputs. The Compound Delivery Model optimizes for the operating metric that must move, then builds systems around it.

10+

YEARS IN DATA

50+

PROFESSIONALS LED

$100K+

ANNUAL SAVINGS

23

AI SERVICES DEPLOYED

01

Ownership Over Handoffs

Roadmap, budget, hiring, and stakeholder alignment are owned end-to-end until a measurable business number moves.

02

Systems Over Heroics

Governance automation, containerized delivery, and data contracts reduce human risk while making scale repeatable.

03

Compound Returns Over Big Bets

Two-week increments are tied to a target metric. If an increment does not move the number, it is removed.

End-to-End Methodology

Unified Data Ecosystem

From raw data to business decisions — six stages, fully automated

Data Sources

APIs · Databases · Streams

Data Pipelines

ETL · Orchestration · Quality

Cloud Infrastructure

VMs · Containers · Storage

AI Agents

LLMs · Automation · Workflows

Advanced Analytics

Dashboards · Reports · Insights

Business Decisions

Strategy · Actions · ROI

12+

VMS & CTS

50+

PIPELINES

100+

API ENDPOINTS

99.9%

UPTIME

Capabilities

What I Deliver

Platform strategy to production deployment. Audit-ready governance, measurable outcomes.

SYS.01
GOVERNANCE

Data Governance & Compliance

delivery focus

Policy-as-code for HIPAA, GDPR, and SOX. Automated controls, lineage tracking, audit-ready documentation.

From

Fragmented compliance, manual audits, regulatory risk

To

Automated policy enforcement with structured audit trails

Structured audit trailsMulti-jurisdictional data residencyAutomated policy enforcement
SYS.02
MLOPS

ML Platform Engineering

delivery focus

Experiment tracking to production inference. Model registries, automated retraining, CI/CD, monitoring.

From

Slow model deployment, scattered experiments, reliability issues

To

Standardized CI/CD for ML with model registry and monitoring

Standardized CI/CD for MLFeature store & model registryReal-time monitoring & observability
SYS.03
HEALTHCARE

Healthcare Data & Analytics

delivery focus

HIPAA-compliant pipelines for patient risk scores, capacity forecasts, PHI-safe executive dashboards.

From

Siloed PHI, compliance concerns, slow insights

To

Real-time patient outcomes modeling with automated PHI protection

Real-time patient outcomes modelingAutomated PHI classification & protectionNetwork capacity & access planning

Next Step

Start the Conversation

Hiring for Head of Data Engineering, platform architecture, or AI infrastructure delivery? Review the resume, selected work, and LinkedIn profile, then book a short intro if there is mutual fit.

Privacy-first intake workflow

Direct Channels

Engagement Areas

  • Head of Data Engineering and analytics leadership
  • Platform modernization and ETL / warehouse rebuilds
  • ML platform engineering and production MLOps
  • Self-hosted infrastructure, observability, and cost control
  • Healthcare and pharma analytics delivery
  • Cross-functional team leadership and delivery cadence

SIMONDATALAB

SimonDatalab is the portfolio and advisory practice of Simon Renauld — a data engineering and analytics leader with 10+ years building production platforms across healthcare, pharma, and high-growth technology environments. The work spans warehouse modernization, MLOps, self-hosted infrastructure, and cross-functional delivery leadership.

Services

  • Data Governance & Compliance
  • ML Platform Engineering
  • Healthcare Data & Analytics
  • Geospatial Intelligence

© 2026 SimonDatalab. Data Engineering Leadership · AI Platform Enablement · Production Infrastructure.

Open to strategic engagements
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Based in Ho Chi Minh City · Supporting teams across North America, Europe, and Asia-Pacific.

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