Data Engineer - QuantumBlack, AI by McKinsey

McKinsey & Company; Paris; Sur place; EUR 45 000 - 70 000; Plein temps

Résumé du poste

A leading company in consulting is seeking a Data Engineer for its Paris office. The role involves building data pipelines, collaborating with cross-functional teams, and contributing to impactful analytics solutions. Ideal candidates will have a strong background in data engineering, programming skills, and a passion for solving complex problems.

Prestations

Comprehensive benefits package

Continuous learning opportunities

Global community of diverse colleagues

Qualifications

• 2+ years of relevant professional experience.
• Ability to write clean, maintainable code in Python, Scala, or Java.
• Proven experience building data pipelines in production.

Responsabilités

• Build and maintain technical platforms for advanced analytics.
• Design and build robust data pipelines for machine learning.
• Map data fields to hypotheses and prepare data for analytics.

Connaissances

Python

Data Engineering

SQL

Machine Learning

Communication

Formation

Degree in computer science, engineering, mathematics

Outils

PySpark

Docker

Kubernetes

AWS

Azure

GCP

Description du poste

Your Growth

Driving lasting impact and building long-term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture - doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward.

In return for your drive, determination, and curiosity, we'll provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible. Your colleagues—at all levels—will invest deeply in your development, just as much as they invest in delivering exceptional results for clients. Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won’t find anywhere else.

When you join us, you will have:

• Continuous learning:Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey.
• A voice that matters:From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes.
• Global community:With colleagues across 65+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences.
• World-class benefits:On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package, which includes medical, dental, mental health, and vision coverage for you, your spouse/partner, and children.

Your Impact

As a Data Engineer in the Paris office, you will work in cross-functional Agile project teams alongside Data Scientists, Machine Learning Engineers, other Data Engineers, Project Managers, and industry experts. You will work hand-in-hand with our clients, from data owners, users, and fellow engineers to C-level executives.

You are a highly-collaborative individual who wants to solve problems that drive business value. You have a strong sense of ownership and enjoy hands-on technical work. Our values resonate with yours.

As a Data Engineer, you will:

• Help to build and maintain the technical platform for advanced analytics engagements, spanning data science and data engineering work.
• Design and build data pipelines for machine learning that are robust, modular, scalable, deployable, reproducible, and versioned.
• Create and manage data environments and ensure information security standards are maintained at all times.
• Understand clients data landscape and assess data quality.
• Map data fields to hypotheses and curate, wrangle, and prepare data for use in advanced analytics models.
• Have the opportunity to contribute to R&D projects and internal asset development.
• Contribute to cross-functional problem-solving sessions with your team and our clients, from data owners and users to C-level executives, to address their needs and build impactful analytics solutions.

Only at McKinsey

Work on real-world, high-impact projects across a variety of industries– Identify micro patterns in data that our clients can exploit to maintain their competitive advantage and watch your technical solutions transform their day-to-day business.

Experience the best environment to grow as a technologist and a leader– Develop a sought-after perspective connecting technology and business value by working on real-life problems across a variety of industries and technical challenges to serve our clients on their changing needs.

Be surrounded by inspiring individuals as part of diverse multidisciplinary teams- Develop a holistic perspective of AI by partnering with the best design, technical, and business talent in the world as your team members.

Our Tech Stack

While we advocate for using the right tech for the right task, we often leverage the following technologies: Python, PySpark, the PyData stack, SQL, Airflow, Databricks, our own open-source data pipelining framework called Kedro, Dask/RAPIDS, container technologies such as Docker and Kubernetes, cloud solutions such as AWS, GCP, and Azure, and more!

Your qualifications and skills

• Degree in computer science, engineering, mathematics, or equivalent experience
• 2+ years of relevant professional experience
• Ability to write clean, maintainable, scalable and robust code in an object-oriented language, e.g., Python, Scala, Java, in a professional setting
• Proven experience building data pipelines in production for advanced analytics use cases
• Experience working across structured, semi-structured and unstructured data
• Ability to communicate complex ideas effectively - both verbally and in writing - in English and French
• Exposure to software engineering concepts and best practices, inc. DevOps, DataOps and MLOps would be considered a plus
• Familiarity with distributed computing frameworks (e.g. Spark, Dask), cloud platforms (e.g. AWS, Azure, GCP), containerization, and analytics libraries (e.g. pandas, numpy, matplotlib)
• Commercial client-facing or senior stakeholder management experience would be beneficial