6 years of experience · Paris

Data Engineer · Tech Lead Data Products — GCP • Looker

Sadjo BARRY

I turn business needs into governed Data Products on Google Cloud Platform (GCP) — from ingestion through to self-service analytics.

6 years in Data Engineering. Currently Tech Lead for business divisions at Renault Group: requirement framing, data modelling, Looker semantic layers, KPI (key performance indicator) governance — plus technical leadership of an offshore Data Engineering team in India.

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  • ~€10M in savings enabled
  • 20 countries
  • 50,000+ users

Paris, France

Portrait of Sadjo BARRY

Measured impact

Results measured in business value.

  • ~€10M

    in savings steered in 2024, underpinned by the TEM project (Travel & Expense Management)

  • 5

    Self-Service Analytics solutions delivered on Looker, including Worldwide Absenteeism rolled out across 20 countries

  • 50,000+

    users of the Learning & Digital dashboards, rolled out worldwide with role-based access governance

  • −20%

    BigQuery cost reduction — query tuning, partitioning, clustering and Dataproc workload rationalisation

  • 4

    business divisions supported: HR (Human Resources), DISG (French acronym for the Real Estate & General Services division), Legal and HSE (Health, Safety, Environment)

Expertise

What I take ownership of

From framing with the business divisions to self-service usage: a single chain of responsibility, five areas of expertise.

  • Data platforms on GCP

    End-to-end design and industrialisation on Google Cloud Platform: ingestion, transformation, orchestration and governed delivery to the business.

    • BigQuery
    • Dataflow
    • Composer
    • Cloud Storage
    • Dataproc

  • Data modelling & Data Products

    Layered Raw › Refined › Data Product models, designed for governed data sharing across several business domains, without duplication.

    • Layered modelling
    • Data Products
    • Cross-domain sharing
    • dbt

  • Data governance & data quality

    Row- and column-level security (RLS/CLS), role-based access, quality checks before anything ships, GDPR (General Data Protection Regulation) compliance and auditability.

    • RLS / CLS
    • Role-based access
    • Quality checks
    • GDPR

  • Looker semantic layer

    Unified LookML as the single source of truth for KPIs: standardised business dimensions, access rules and consistent self-service analytics.

    • Looker
    • LookML
    • KPIs
    • Self-service analytics

  • SQL & Python

    Advanced SQL and BigQuery optimisation — partitioning, clustering, query strategies — and Python pipelines orchestrated with Airflow.

    • Advanced SQL
    • Python
    • Airflow
    • BigQuery optimisation

A Tech Lead who speaks the language of the business and still writes the code.

  • Two hats

    I frame with leadership, design the architecture and deliver hands-on when needed — as on the TEM project.

  • Results in euros

    Savings, freed-up person‑days, shorter lead times: every Data Product is measured on business value.

  • Security by design

    Row- and column-level security, GDPR, auditability: every user sees only what they are authorised to see.

  • Controlled offshore delivery

    A team in India delivering to standard, aligned with business priorities in France.

Case studies

Two Data Products, from source to decision.

Human Resources division · 20 countries

Worldwide Absenteeism — WTM (Workforce Time Management)

Design and technical leadership of a worldwide HR analytics platform covering absenteeism across several Renault Group countries.

Owner of the end-to-end value chain: HR data ingestion and transformation, BigQuery modelling, governance, row- and column-level security (RLS/CLS), KPI computation, Looker semantic layers, self-service dashboards and access management.

  • 20 countries
  • Per-employee access
  • 1 of 5 Looker solutions delivered

Every employee sees only the data they are authorised to see: security lives in the model, not in the dashboards — so the solution stays robust as each new country or user is added.

Architecture — from source to self-service

  1. Sources

    Country HRIS (HR information systems)

    Multi-country extracts, heterogeneous formats and reference data

  2. Ingestion

    Cloud Storage · Dataflow · Composer

    Orchestrated pipelines, quality checks, replays and traceability

  3. Modelling

    BigQuery — Raw › Refined › Data Product

    Partitioning, clustering, RLS/CLS security, absenteeism KPI computation

  4. Semantic

    Looker / LookML

    KPI source of truth, business dimensions, role-based access

  5. Usage

    HR Self-Service Analytics

    Country dashboards and autonomous exploration by the HR teams

  • Governance

    Row- and column-level security, role-based access, KPIs defined once and reused everywhere.

  • Cross-domain sharing

    Data models designed for governed sharing across several business domains, without duplication.

  • Business autonomy

    HR teams explore their own indicators without going through the Data team: fewer ad hoc requests, faster decisions.

DISG — Real Estate & General Services division

TEM project — Travel & Expense Management

A single foundation to steer travel and expense spend, joining accounting data, the HR reference (functions, job families, grade levels), worked days and remote work, and feeds from the Emburse and Amex GBT (American Express Global Business Travel) business tools.

  • ~€10M

    in savings steered over the first three quarters of 2024, based on TEM data

  • 2–3 person‑days

    freed every month: direct Emburse → Renault feed, weekly refresh instead of monthly

  • The 20th

    of the month: Amex GBT reporting published instead of the 25th–30th, supplier workload halved

  • ~70%

    of spend covered by the reference budget — a shortfall surfaced and confirmed by the Finance division

  • Expense controls. Duplicate detection, actual/flat-rate overlaps, host/guest cross-checks on business meals — controls intended for the control plan of the AFA (French Anti-Corruption Agency).

  • Compliance & continuity. Integration into the Renault Datalake with GDPR and security declarations: traced access, auditable reports for URSSAF (French social security collection agency) and the AFA.

  • AI (artificial intelligence) ready. Data structured for automated receipt analysis and budget projection under the travel policy.

Tech Lead & offshore coordination

A Paris ↔ Chennai axis, held together by written standards.

I connect the business divisions in France with the Data Engineering team at RNTBCI (Renault Nissan Technology & Business Centre India) in Chennai.

  • Delivery quality.

    Code and modelling reviews, explicit acceptance criteria, data quality checks before anything reaches the business.

  • Shared standards.

    Documented development conventions, modelling patterns and best practices, so deliverables look the same whoever wrote them.

  • Business alignment.

    Continuous backlog prioritisation with the divisions, so development serves real stakes rather than the queue.

  • Distributed work.

    Rituals adapted to the time difference, written specifications, traceability in Jira and Confluence.

Setup

Role
Tech Lead / technical leadership
Team
Data Engineering — RNTBCI, Chennai (India)
Scope
HR, DISG, Legal & HSE
Setting
Agile, international projects

Experience

6 years at Renault Group, from Data Engineer to Tech Lead.

  1. – present

    Renault Group

    Data Engineer · Tech Lead — Data Products (GCP • Looker)

    • Supporting the HR, DISG, Legal and HSE divisions in formalising, prioritising and translating their analytics needs into Data solutions on GCP.
    • Leading the design, industrialisation and rollout of the Data Products in my scope, from business framing to delivery.
    • Technical leadership of an offshore Data Engineering team (RNTBCI, India): delivery quality, coordination, alignment with business priorities.
    • Defining development, modelling and governance standards; Looker (LookML) semantic layers and BigQuery performance optimisation.

  2. –

    Renault Group

    Data Engineer — Data Platform & BI (Business Intelligence)

    • Translating business needs into scalable Data solutions on GCP (BigQuery, Cloud Storage, Dataflow) and building end-to-end pipelines.
    • Spotfire administration, structuring of secured Information Links and deployment processes ensuring governed access to data.
    • International projects in an Agile setting, progressively taking on complex topics up to becoming the go-to technical expert.
    • Maintenance, optimisation, incident analysis and user support to keep the Data solutions available.

Stack & education

Everyday tools.

Google Cloud & data

  • BigQuery
  • Dataflow
  • Composer / Airflow
  • Cloud Storage
  • Dataproc
  • Advanced SQL
  • Python
  • dbt

Modelling & BI

  • Looker / LookML
  • Data modelling
  • Data governance
  • Row / column-level security (RLS/CLS)
  • Spotfire (admin)
  • Dynatrace

Industrialisation & method

  • Git / GitLab
  • CI/CD (continuous integration & delivery)
  • Docker
  • Linux
  • Jira
  • Confluence
  • Agile

Education

  • Engineering degree in Computer Science

    Sup Galilée — Université Sorbonne Paris Nord

    2019–2022

  • BSc in Mathematics & Computer Science

    Faculty of Sciences — Université Ibn Zohr

    2016–2019

Selected achievements

  • TEM project (Travel & Expense Management): the steering foundation behind ~€10M in 2024 savings.

  • 5 Self-Service Analytics solutions on Looker, including Worldwide Absenteeism rolled out in 20 countries, with governed data sharing across business domains.

  • A unified Looker/LookML semantic layer: the KPI source of truth and the basis for access security.

  • Worldwide rollout of the Learning & Digital dashboards to over 50,000 users, with role-based access governance.

Contact

Let’s talk Data.

Data platforms on GCP, Looker semantic layers, data governance: I’m always glad to share lessons learned and good practices with teams and professionals in the field. The easiest way to reach me is LinkedIn.

Contact me on LinkedIn(opens in a new tab)Download the CVPDF · EnglishGitHub(opens in a new tab)

Paris, France