Big Data Architect (m/w/d)

As a Big Data Architect (m/w/d) at Sopra Steria, your main responsibilities will include consulting with clients from requirements gathering to the implementation and operational deployment of solutions. You will also design and develop Big Data and Data Science use cases, and create technical architectures and BI solutions tailored to client needs, navigating both agile and traditional project methodologies.

To become a Big Data Architect (m/w/d) at Sopra Steria, you’ll need a degree in Computer Science, Mathematics, or related fields, along with demonstrable experience in designing Big Data architectures. Proficiency in programming with languages like Python, Java, or Scala is essential, plus experience working with agile methodologies and DevOps tools.

Sopra Steria is committed to supporting the professional growth of Big Data Architects through a range of continuous training and certification opportunities. You'll also participate in mentorship programs and be engaged in innovative projects, allowing you to leverage modern technologies while fostering your career in a collaborative environment.

As a Big Data Architect (m/w/d) at Sopra Steria, familiarity with technologies such as Docker, Kubernetes, and data architecture concepts like Data Lakehouse and Data Mesh is crucial. Being adept with pipelines and DevOps practices will also set you up for success in this role.

In your role as a Big Data Architect (m/w/d), you will work on a variety of projects that range from data analytics solutions to bespoke BI systems. Your work will largely determine how our clients utilize data effectively, providing them with insights that drive business outcomes while also engaging in socially impactful initiatives.

To gather requirements effectively, I would initiate discussions with key stakeholders to understand their business objectives and data needs. I would employ techniques such as interviews, workshops, and surveys to ensure comprehensive coverage of the requirements, prioritizing communication and collaboration throughout the process.

I have extensive experience with Python and Java, having used both languages for developing data processing pipelines and machine learning models in various projects. Additionally, my familiarity with libraries and frameworks such as Pandas and Spark has enabled me to solve complex data challenges efficiently.

My approach to designing data architecture begins with understanding the project requirements and goals. I would analyze current data sources and decide on the most suitable architecture—whether that be a Data Lakehouse or traditional database. It’s essential to ensure scalability, security, and ease of access in my designs.

I have worked extensively with Agile methodologies, primarily Scrum, allowing for iterative development and flexibility in project scope. This experience has helped me foster strong team collaboration and ensure timely delivery of project milestones, adapting to changes based on stakeholder feedback.

I believe in integrating DevOps practices early in the project lifecycle. This includes continuous integration and continuous deployment (CI/CD) pipelines to automate testing and deployment, as well as fostering a culture of collaboration between development and operations teams, which streamlines processes and enhances product quality.

In a previous role, I led a project to develop a real-time dashboard for a retail client that aggregated data from multiple sources. By leveraging advanced visualizations and analytics, we provided insights into sales trends and customer behavior, which significantly improved decision-making, ultimately increasing sales by 15%.

One of the biggest challenges is managing the complexity of data ecosystems and ensuring data quality across various sources. Additionally, aligning technical solutions with business objectives requires constant communication with stakeholders to ensure that the project is delivering real value.

I stay updated by participating in webinars, online courses, and industry conferences. I also follow influential thought leaders on social media and read relevant journals and publications to keep abreast of emerging trends and best practices in Big Data and analytics.

Collaboration is crucial in my role as it fosters diverse perspectives and enhances problem-solving. I frequently liaise with various teams, including data scientists, business analysts, and stakeholders, to ensure we are aligned on objectives and to leverage each other’s expertise effectively.

In situations where project requirements change, I would assess the impact of those changes on the overall project timeline and resources. I would communicate transparently with the team and stakeholders, adjusting plans and expectations while ensuring we remain focused on delivering value aligned to the new requirements.