Logiciel helps enterprises design, build and operate analytics engineering systems that connect data platforms with business decision-making. From semantic layers and BI-ready data models to cloud data architecture, data pipelines, governance, observability and managed analytics operations, we help teams create reliable reporting foundations that product, finance, operations and leadership can trust.

Book an Intro Call Common Questions

5 steps

Analytics engineering framework

3 models

Engagement models to deliver the work

Why Logiciel

Why Analytics Engineering Matters for Enterprise Decision-Making.

Why Logiciel · 01

Teams define the same metric differently across reports.

Why Logiciel · 02

Data analysts spend too much time cleaning data instead of analysing it.

Why Logiciel · 03

Business users do not trust dashboards when numbers do not match.

Why Logiciel · 04

Data models grow without shared standards or clear ownership.

Why Logiciel · 05

Cloud data architecture becomes harder to manage as platforms scale.

Why Logiciel · 06

Analytics pipelines lack observability, testing and documentation.

Why Logiciel · 07

AI initiatives struggle when reporting data is not trusted or well-structured.

What you get

What You Get When You Work With Logiciel on Analytics Engineering.

We build analytics engineering foundations that make enterprise data easier to model, govern and use.

A clear analytics engineering roadmap tied

to business priorities

Trusted data models

for finance, operations, product, sales and customer teams

Semantic layers

that standardise metrics, dimensions and business definitions

Cloud data architecture

designed for scalable analytics and AI-ready workflows

Data quality checks, lineage and observability

built into analytics pipelines

Governance, access controls and documentation

for reliable reporting

A practical analytics operating model your teams can maintain after launch

What we build

Analytics Engineering Solutions Built for Enterprise Workloads.

Analytics Strategy and Roadmap

Current-state assessment, reporting needs, KPI alignment, modelling priorities and phased analytics implementation planning.

Semantic Layer Engineering

Trusted metric definitions, dimensions, entities, business logic and reusable semantic models across BI and analytics tools.

Data Analytics Engineering

Data modelling, transformation workflows, curated datasets, testing and documentation for reliable business reporting.

Cloud Data Architecture

Cloud architecture design for warehouses, lakehouses, data lakes, pipelines, storage, compute and analytics access layers.

Cloud Architecture Services

Cloud platform architecture, cloud based architecture, cloud computing architecture and cloud native application architecture for modern data environments.

AWS Data Lake Architecture

AWS data lake architecture, AWS security architecture and AWS cloud architect practices for scalable analytics foundations.

Managed Analytics Operations

Ongoing monitoring, incident response, data quality reviews, cost control, performance tuning and continuous improvement.

Engagement

Engagement Models Designed for Analytics Engineering Services Delivery.

Dedicated Analytics Engineering Squad

A standing team of analytics engineers, data engineers, cloud solutions architects and platform specialists embedded into your analytics roadmap.

Engagement

Analytics Advisory and Staff Augmentation

Senior analytics engineers, data analytics engineers and cloud architecture consulting specialists who strengthen your internal data, product or platform teams.

Engagement

Outcome-Based Analytics Engineering

Fixed-scope engagements with defined analytics outcomes, delivery milestones and success baselines agreed up front.

Engagement

Under the hood

Analytics Engineering Services We Deliver.

Analytics Engineering Diagnostic and Roadmap

What it meansDetailed assessment of dashboards, datasets, metrics, data models, pipelines, BI tools, cloud architecture and reporting pain points.

Semantic Layer and Metrics Engineering

What it meansMetric standardisation, KPI modelling, dimensional modelling, reusable business logic, data marts and trusted reporting foundations.

Data Transformation and Modelling

What it meansAnalytics-ready transformations, curated datasets, testing workflows, documentation, lineage and environment promotion.

Cloud Data and Platform Architecture

What it meansCloud data architecture, cloud platform architecture, cloud architecture design and cloud based microservices for scalable analytics systems.

AWS Analytics and Data Lake Architecture

What it meansAWS cloud solution architect support for data lakes, warehouses, pipelines, access controls, security architecture and analytics workloads.

Data Quality, Governance and Observability

What it meansValidation checks, schema monitoring, freshness tracking, lineage mapping, access controls, auditability and data quality dashboards.

Managed Analytics Engineering Operations

What it meansOngoing pipeline monitoring, dashboard reliability support, metric reviews, cost reporting, performance tuning and continuous improvement.

Insights

Analytics Engineering Services Insights & Frameworks.

Patterns from our data platform engineering teams that help enterprises improve metric trust, analytics speed and reporting reliability.

Enterprise Analytics Operating Model

How we structure ownership, metric governance, semantic layer reviews, data quality checks and continuous improvement across analytics teams.

↳ Insights

Analytics Engineering Readiness Framework

A practical approach to ranking analytics priorities by business impact, metric inconsistency, data quality risk, cloud architecture maturity and AI readiness.

↳ Insights

How we work

Our Analytics Engineering Services Framework.

Analytics Diagnostic and Baseline

We assess dashboards, reports, metrics, data models, pipelines, cloud platforms, governance controls and business priorities.

Metric and Data Flow Mapping

We map core KPIs, source systems, transformation logic, ownership, reporting dependencies and downstream analytics workflows.

Analytics Model and Platform Engineering

We build semantic layers, curated datasets, transformations, cloud data architecture, BI-ready models and secure access foundations.

Reliability, Governance and Observability

We harden analytics systems with testing, lineage, freshness checks, access controls, documentation, alerts and operational dashboards.

Analytics Operating Model

We hand over a repeatable analytics engineering practice, including ownership, KPIs, governance reviews, runbooks and improvement cadences.

Selected work

Tailored engineering for your industry.

Zeme · Real Estate Cut development costs 50% and launched 3× faster with dedicated dev teams.

Real Estate

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Leap · Construction Scaled to 7-figure ARR with AI-augmented software teams.

Construction

Scaled to 7-figure ARR with AI-augmented software teams.

KW · Real Estate 56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Real Estate

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Resources

Insights for tech leaders.

Framework · PDF

80% Of Engineering Organisations Will Run A Platform Team. The Ticket Queue Survives.

Whitepaper · PDF

40% Of Agent Projects Get Cancelled. Five Engineering Failures Explain Why

Whitepaper · PDF

The State of AI-Assisted Engineering 2026

In their words

What our clients say.

Teams that needed to ship fast, and did. Here's what partnering with Logiciel felt like from the inside.

I would highly recommend them to anyone looking to scale quickly or needing support in engineering, product, or QA.

Patrick Fingles

CEO, Leap

We don't just call them Logiciel; they're part of the Zeme team. Within the first week they were contributing meaningfully to our codebase.

Elior Alayev

Founder & CEO, Zeme

The Logiciel team worked tirelessly and built everything we needed, with security and best practices across our entire platform. It allowed us to become #1 in our industry, and we couldn't have done it without them.

David Buzzelli

Co-Founder, JobProgress

Questions

Frequently asked questions.

What does Analytics Engineering Services include?

Analytics Engineering Services include analytics strategy, semantic layer development, data modelling, transformation workflows, BI-ready datasets, cloud data architecture, governance, observability and managed analytics operations.

What does an analytics engineer do?

An analytics engineer turns raw data into trusted, reusable models for reporting and decision-making. They build semantic layers, define metrics, create transformation workflows, document logic and ensure business teams can trust analytics outputs.

How is a data analytics engineer different from a data engineer?

A data engineer focuses on moving, storing and processing data. A data analytics engineer focuses on modelling that data into business-ready metrics, datasets and reporting layers that analysts and decision-makers can use.

Can Logiciel support cloud architecture design for analytics platforms?

Yes. We support cloud architecture design, cloud data architecture, cloud platform architecture, cloud computing architecture and cloud architecture consulting for scalable analytics, reporting and AI-ready data systems.

Do you support AWS data lake architecture?

Yes. Logiciel supports AWS data lake architecture, AWS security architecture, AWS cloud solution architect guidance and cloud data platform engineering for analytics and AI workloads.

How long does Analytics Engineering Services typically take?

Most engagements produce a diagnostic, roadmap and initial analytics engineering foundation within 4-8 weeks, while larger analytics programs run across phased implementation waves over several months.

Who owns the deliverables from an Analytics Engineering Services engagement?

You retain ownership of all data models, semantic layers, transformations, pipelines, dashboards, governance assets, cloud architecture documentation, runbooks and implementation materials.

Do you support ongoing analytics operations after launch?

Yes. We run managed operations with monitoring, incident response, data quality reviews, metric governance, cost tracking, performance tuning and continuous improvement.

Let's build

Accelerate Analytics Engineering Services.

Ready to turn Analytics Engineering Services into a trusted foundation for reporting, automation and AI? Partner with Logiciel to build semantic layers, modern cloud data architecture and analytics systems your teams can rely on.

Start an analytics engineering assessment

Analytics Engineering Services.

See Logiciel in action.

Why Analytics Engineering Matters for Enterprise Decision-Making.

Teams define the same metric differently across reports.

Data analysts spend too much time cleaning data instead of analysing it.

Business users do not trust dashboards when numbers do not match.

Data models grow without shared standards or clear ownership.

Cloud data architecture becomes harder to manage as platforms scale.

Analytics pipelines lack observability, testing and documentation.

AI initiatives struggle when reporting data is not trusted or well-structured.

What You Get When You Work With Logiciel on Analytics Engineering.

A clear analytics engineering roadmap tied

Trusted data models

Semantic layers

Cloud data architecture

Data quality checks, lineage and observability

Governance, access controls and documentation

A practical analytics operating model your teams can maintain after launch

Analytics Engineering Solutions Built for Enterprise Workloads.

Analytics Strategy and Roadmap

Semantic Layer Engineering

Data Analytics Engineering

Cloud Data Architecture

Cloud Architecture Services

AWS Data Lake Architecture

Managed Analytics Operations

Engagement Models Designed for Analytics Engineering Services Delivery.

Dedicated Analytics Engineering Squad

Analytics Advisory and Staff Augmentation

Outcome-Based Analytics Engineering

Analytics Engineering Services We Deliver.

Analytics Engineering Diagnostic and Roadmap

Semantic Layer and Metrics Engineering

Data Transformation and Modelling

Cloud Data and Platform Architecture

AWS Analytics and Data Lake Architecture

Data Quality, Governance and Observability

Managed Analytics Engineering Operations

Analytics Engineering Services Insights & Frameworks.

Enterprise Analytics Operating Model

Analytics Engineering Readiness Framework

Our Analytics Engineering Services Framework.

Analytics Diagnostic and Baseline

Metric and Data Flow Mapping

Analytics Model and Platform Engineering

Reliability, Governance and Observability

Analytics Operating Model

Tailored engineering for your industry.

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Scaled to 7-figure ARR with AI-augmented software teams.

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Insights for tech leaders.

80% Of Engineering Organisations Will Run A Platform Team. The Ticket Queue Survives.

40% Of Agent Projects Get Cancelled. Five Engineering Failures Explain Why

The State of AI-Assisted Engineering 2026

What our clients say.

Frequently asked questions.

Accelerate Analytics Engineering Services.