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Mid BI Engineer

hace 4 semanas


Barcelona, Barcelona, España Factorial A tiempo completo

Hi there

As Factorial continues on our mission of boosting the next generation of people-first SMB's, we're hiring a
BI Engineer
to help.

The Team and you:

Factorial's mission is to empower businesses to make better decisions, and data is a key component of that mission. BI Engineers are responsible for unlocking data and insights for all Factorial teams. You will work closely with Analytics Engineers in managing data sets, internal and external databases, and processes in AWS Platform.

The Role

As a BI Engineer, you'll be the
owner of Power BI
and a key contributor to our data modelling ingestion.

In this role, you'll work closely with the
People & Culture
team, playing a pivotal part in transforming how we use data to make decisions about our most valuable asset — our people. You will:

  • Support business leaders with actionable insights

  • Ensure our
    data lake
    integrates data from all relevant sources

  • Own the
    end-to-end data pipeline
    , with a strong focus on
    data quality

What you will do:

  • Build insights from complexity
    : Analyze qualitative and quantitative data to uncover trends, opportunities, and blind spots in our people processes — transforming them into actionable initiatives.

  • Own the E2E data
    — from ingestion to optimal modelling — designing and managing dashboards, reports, and KPIs that give visibility into employee lifecycle metrics

  • Perform analysis on large datasets using SQL, Python, and other relevant tools

  • Maintain and optimize dashboards for senior leadership and C-level stakeholders

  • Work with cross-functional teams to ensure every deliverable meets the highest quality standards

Your background:

  • 4+ years
    of hands-on experience with
    Power BI

  • Strong knowledge of
    SQL
    and
    Python

  • 4+ years
    building and maintaining
    ETL pipelines

  • Experience with
    DBT
    ,
    AWS
    , and
    GitHub
    version control

  • Data-driven mindset with strong analytical and problem-solving skills

  • Self-starter with excellent time management and prioritization skills

  • Comfortable in fast-paced, high-performance environments

  • Team player with a positive attitude

Nice to have:

  • Previous experience collaborating with the
    People Team

  • Experience in
    Data Science
    projects, especially with large language models (LLMs) and machine learning

  • Understand
    HRIS systems
    (e.g.,
    Factorial
    , Workday, HiBob, or similar) and have experience extracting and analyzing HR-related data.

For this mid-level position, the annual gross salary starts at
42.800€.
This includes a
base salary of 40,000€ and a variable pay of up to 2.800€ annually
, depending on the company's goals. We top it with an Employee Stock Options Plan.

To wrap up:

  • We're seeking an experienced end-to-end Data Analyst with exceptional analytical skills and a deep understanding of the entire data pipeline

  • From 53,500€ Salary + ESOP plan.

  • 1.000€ of annual Learning and Development budget.

  • A safe and diverse environment to grow and make an impact.

And when at the office...

  • Breakfast in the office and organic fruit.

  • Nora and Apetit discounts.

  • Pet Friendly.

About us

Factorial is an all-in-one HR Software fast-growing company founded in 2016. Our mission is to help SMEs automate HR workflows, centralize people's data, and make better business decisions. Currently, we serve thousands of customers in over 60 countries worldwide and across industries, and we have built a diverse and multicultural team of over 900 people in our Barcelona, Brazil, Mexico, and US offices.

Our Values

  • We own it:
    We take responsibility for every project. We make decisions, not excuses.

  • We learn and teach:
    We're dedicated to learning something new every day and, above all, sharing it.

  • We partner:
    Every decision is a team decision. We trust each other.

  • We grow fast:
    We act fast. We believe that the worst mistake is not learning from them.

Wanna learn more about us? Check our website