Descripción de la oferta
Data Platform Engineer Specialist
Location: Barcelona
About Sanofi:
We are Sanofi, an innovative global healthcare company that chases the miracles of science to improve people's lives. As an R&D‑driven, AI‑powered biopharma leader, we work across 100 countries to transform the practice of medicine by turning the impossible into the possible. Our expertise spans vaccines, rare diseases, rare blood disorders, neurology, immunology, oncology, diabetes, and cardiovascular diseases. Guided by our core values – Aim Higher, Act for Patients, Be Bold, and Lead Together – we provide life‑changing treatment options and life‑saving vaccine protection to millions globally while putting sustainability and social responsibility at the center of our ambitions.
Who You Are:
You bring a wealth of experience as a data or cloud engineer, playing an integral role in designing and developing robust solutions that elevate business operations. Your expertise spans both back‑end and front‑end technologies, and you have a proven record of building scalable, highly functional systems. With a keen eye for user experience, you prioritize meeting user needs through agile development and close collaboration with stakeholders. You’re a clear communicator who thrives in cross‑functional teams, enjoys working with subject matter experts, mentors and supports your colleagues, and consistently delivers professional‑grade solutions. Driven by curiosity, you’re always eager to learn, innovate, and improve.
Position summary:
Join Sanofi’s data platform engineering team and play a key role in advancing analytics, AI, and ML initiatives that shape the future of healthcare. As a Data Engineer, you’ll design and deliver scalable solutions, mentor fellow engineers, and collaborate closely with leadership and teams to address both immediate and long‑term business needs. Enjoy opportunities to learn from experts across technical and business domains, and drive impactful, data‑driven innovation.
Key Responsibilities:
Collaborate closely with experienced data engineers, data scientists, and stakeholders to deeply understand complex data needs—especially in crucial domains like bioinformatics, omics, and clinical data.
Design, build, and maintain robust data infrastructure on AWS Cloud in support of our data mesh architecture.
Automate, optimize, and fine‑tune platform provisioning, scaling, and maintenance tasks to boost operational efficiency, performance, scalability, and cost control.
Lead the development and optimization of data pipelines, leveraging your expertise in data integration, ETL/ELT, advanced tooling and AWS cloud to deliver cutting‑edge solutions.
Implement data monitoring and alerting systems, and partner with DevOps teams to proactively identify and resolve data issues.
Ensure data security, compliance, and governance at every stage of the data platform, following global standards and best practices.
Establish and enforce global data engineering standards, ensuring alignment with data architecture, platform, quality, and governance principles.
Demonstrate deep expertise in implementing data warehouses, data lakes, and distributed processing technologies—such as Spark, Hadoop, and Kafka—in production environments.
Showcase your advanced proficiency with SQL (preferably Snowflake) and both relational and non‑relational databases to optimize complex queries and data manipulation.
Exhibit mastery in programming languages (like Python, Shell scripting, and Scala/Java) for the development of sophisticated data engineering solutions.
Work within cross‑functional agile teams to architect and deploy hybrid‑cloud solutions and automated pipelines, ensuring seamless and high‑performance data processing.
Document data infrastructure design, configuration, and processes thoroughly for reference and training purposes.
Act as a mentor and leader, offering guidance and support to junior engineers, and fostering a culture of collaboration and growth within the team.
Engage actively within the data engineering community by sharing insights, best practices, and innovations that contribute to broader industry progress.
Qualifications:
Bachelor's/Master's in STEM or a relevant field with 4–6 years of experience in data engineering, with a strong preference for experience in the life sciences/pharmaceutical industry.
Extensive background in designing, developing, and optimizing data and cloud solutions, including data pipelines, service‑oriented architectures.
Strong knowledge of data integration technologies, ETL/ELT, and modern data engineering technologies, with experience in implementing or supporting Data Mesh architectures.
Experience with multimodal data systems and architectures, including batch, near real‑time, and streaming data.
Proven experience designing distributed architectures for large‑scale data processing with high performance, scalability, and fault tolerance (AWS, Snowflake, Spark, Hadoop, Kafka).
Advanced knowledge of SQL, relational/non‑relational databases, and data query optimization. Proficiency in programming languages such as Python, Shell scripting, and Scala/Java.
Expertise in managing cloud‑native systems following IaC and DataOps principles (terraform, CI/CD, Orchestration, Actions).
Extensive experience with agile development processes and concepts.
Exceptional problem‑solving skills and attention to detail.
Excellent communication, presentation, and interpersonal skills.
Ability to lead teams effectively and collaborate with stakeholders at all levels.
Curiosity and a commitment to continuous learning and improvement.
Nice to Haves:
Experience in the life sciences/pharmaceutical industry.
Familiarity with Data Mesh concepts such as data as a product, domain‑driven design, and federated computational governance.
Experience building AI agents, chatbots, conversational AI or machine learning applications.
Familiarity with AI/ML concepts and how to present AI/ML‑driven insights to users.
Familiarity with visualization tools (PowerBI, Tableau) and project management tools (JIRA, Confluence).
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