Data /ML Cloud Engineer

Syensqo is all about chemistry. We’re not just referring to chemical reactions here, but also to the magic that occurs when the brightest minds get to work together. This is where our true strength lies. In you. In your future colleagues and in all your differences. And of course, in your ideas to improve lives while preserving our planet’s beauty for the generations to come.

Join us at Syensqo, where our IT team is gearing up to enhance its capabilities. We play a crucial role in the group's transformation—accelerating growth, reshaping progress, and creating sustainable shared value. IT team is making operational adjustments to supercharge value across the entire organization.

Here at Syensqo, we're one strong team! Our commitment to accountability drives us as we work hard to deliver value for our customers and stakeholders. In our dynamic and collaborative work environment, we add a touch of enjoyment while staying true to our motto: reinvent progress.

Come be part of our transformation journey and contribute to the change as a future team member.

We are looking for:

As a Data/ML Engineer, you will play a central role in defining, implementing, and maintaining cloud governance frameworks across the organization. You will collaborate with cross-functional teams to ensure secure, compliant, and efficient use of cloud resources for data and machine learning workloads. Your expertise in full-stack automation, DevOps practices, and Infrastructure as Code (IaC) will drive the standardization and scalability of our cloud-based data and ML platforms.

Key requirements are:

• 
  Ensuring cloud data governance

  • Define and maintain central cloud governance policies, standards, and best practices for data, AI and ML workloads
  • Ensure compliance with security, privacy, and regulatory requirements across all cloud environments
  • Monitor and optimize cloud resource usage, cost, and performance for data, AI and ML workloads

• 
  Design and Implement Data Pipelines

  • Co-develop, co-construct, test, and maintain highly scalable and reliable data architectures, including ETL processes, data warehouses, and data lakes with the Data Platform Team

• 
  Build and Deploy ML Systems

  • Co-design, co-develop, and deploy machine learning models and associated services into production environments, ensuring performance, reliability, and scalability

• 
  Infrastructure Management

  • Manage and optimize cloud-based infrastructure (e.g., AWS, Azure, GCP) for data storage, processing, and ML model serving

• 
  Collaboration

  • Work collaboratively with data scientists, ML engineers, security and business stakeholders to align cloud governance with organizational needs
  • Provide guidance and support to teams on cloud architecture, data management, and ML operations.
  • Work collaboratively with other teams to transition prototypes and experimental models into robust, production-ready solutions

• 
  Data Governance and Quality:

  • Implement best practices for data governance, data quality, and data security to ensure the integrity and reliability of our data assets.

• 
  Performance and Optimisation:

  • Identify and implement performance improvements for data pipelines and ML models, optimizing for speed, cost-efficiency, and resource utilization.

• Monitoring and Alerting

  • Establish and maintain monitoring, logging, and alerting systems for data pipelines and ML models to proactively identify and resolve issues

• Tooling and Automation

  • Design and implement full-stack automation for data pipelines, ML workflows, and cloud infrastructure
  • Build and manage cloud infrastructure using IaC tools (e.g., Terraform, CloudFormation)
  • Develop and maintain CI/CD pipelines for data and ML projects
  • Promote DevOps culture and best practices within the organization
  • Develop and maintain tools and automation scripts to streamline data operations, model training, and deployment processes

• Stay Current on new ML / AI trends:

  • Keep abreast of the latest advancements in data engineering, machine learning, and cloud technologies, evaluating and recommending new tools and approach
  • Document processes, architectures, and standards for knowledge sharing and onboarding

Education and experience

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field. (Relevant work experience may be considered in lieu of a degree).
• Programming: Strong proficiency in Python (essential) and experience with other relevant languages like Java, Scala, or Go.
• Data Warehousing/Databases: Solid understanding and experience with relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra). Experience with data warehousing solutions (e.g., Snowflake, Redshift, BigQuery) is highly desirable.
• Big Data Technologies: Hands-on experience with big data processing frameworks (e.g., Spark, Flink, Hadoop).
• Cloud Platforms: Experience with at least one major cloud provider (AWS, Azure, or GCP) and their relevant data and ML services (e.g., S3, EC2, Lambda, EMR, SageMaker, Dataflow, BigQuery, Azure Data Factory, Azure ML).
• ML Concepts: Fundamental understanding of machine learning concepts, algorithms, and workflows.
• MLOps Principles: Familiarity with MLOps principles and practices for deploying, monitoring, and managing ML models in production.
• Version Control: Proficiency with Git and collaborative development workflows.
• Problem-Solving: Excellent analytical and problem-solving skills with a strong attention to detail.
• Communication: Strong communication skills, able to articulate complex technical concepts to both technical and non-technical stakeholders.

Bonus Points (Highly Desirable Skills & Experience):

• Experience with containerisation technologies (Docker, Kubernetes).
• Familiarity with CI/CD pipelines for data and ML deployments.
• Experience with stream processing technologies (e.g., Kafka, Kinesis).
• Knowledge of data visualization tools (e.g., Tableau, Power BI, Looker).
• Contributions to open-source projects or a strong portfolio of personal projects.
• Experience with [specific domain knowledge relevant to your company, e.g., financial data, healthcare data, e-commerce data].

Language skills

Fluent English

What’s in it for the candidate

• Be part of a highly motivated team of explorers
• Help make a difference and thrive in Cloud and AI technology
• Chart your own course and build a fantastic career
• Have fun and enjoy life with an industry leading remuneration pack