Data & AI Engineer (Freelance)

Job ID:23002                                                                                                                                       Location: Brussels

Your Impact

The Data & AI Engineer in the data team of our client is responsible for building systems and pipelines which allow to handle data at scale. These systems allow developing advanced algorithms and data pipelines to transform raw data into fully automated data products. This role demands application of advanced data processing technologies, methodologies and techniques, including Machine Learning. The mission is to simplify data access for business users and data department members, keeping business objectives aligned when handling large datasets. The secondary mission is to industrialize advanced machine learning algorithms by establishing and maintaining an MLOps framework in collaboration with the data department and IT teams.

Reporting Line

The Data & AI Engineer reports to the Data & AI Tech Lead and is part of the Data & AI feature team.

Responsibilities

1. Data & AI Systems Design, Management, and Monitoring (20% time spent):

• Design and build robust, scalable, and efficient systems for collecting, storing, and analysing data at scale.
• Collaborate with cross-functional teams to implement best practices in data intensive applications design across the org
• Incorporate DevOps principles for continuous delivery and system maintenance.
• Lead the development, maintenance, and improvement of the MLOps framework in collaboration with data scientists and the IT department.
• Monitor data systems in production, ensuring high availability and performance, and compliance with SLAs.

2. Data Acquisition, Transformation, and Democracy (30% time spent):

• Understand the needs of the data team and final business users in terms of data and data structure.
• Develop pipelines to provide the necessary data in the required format/structure.
• Promote data democracy by making the needed data available in a self-serve mode and helping in deploying and maintaining Data Visualization and Reporting solutions.

3. Automation, Code Management, and Compliance (30% time spent):

• Identify data-related processes that can be automated within the Data & Analytics team and develop the required CI/CD pipelines for deployment and testing
• Act as a gatekeeper for code versioning and coding standards across the Data & Analytics team.
• Ensure compliance with data and coding standards, policies, and procedures.

4. Stakeholder Management and Communication (10% time spent):

• Act as an intermediary between final data users (Business or D&A department members) and the IT department.
• Understand, challenge, and translate business needs to IT to derive cutting-edge solutions.
• Handle complexities and negotiate with different departments to challenge the status quo and achieve optimal results.

5. Change Management and SLA Compliance (10% time spent):

• Develop and implement change management procedures to ensure smooth transitions when implementing new data systems, pipelines, or processes.
• Manage the human side of change, including user adoption and resistance, to increase the successful implementation of new practices and procedures.
• Monitor SLA compliance and work with stakeholders to address any deviations.

Competencies

• Technical Expertise: Proficiency in relevant programming languages (like Python, Scala, or Java), data processing software (such as Hadoop, Spark, Flink), database languages (like SQL), cloud platforms (like AWS, GCP or Azure), and data pipeline orchestration tools (such as Airflow or Luigi).

• Data Modeling and Management: Deep understanding of data modeling concepts, and the ability to effectively manage, manipulate and analyze large or complex datasets.

• Machine Learning Knowledge: An understanding of machine learning algorithms and processes, and the ability to work with data scientists to operationalize these models.

• System Design and DevOps: Strong skills in designing scalable and efficient data systems, incorporating DevOps principles for system management and continuous delivery.

• Analytical Thinking: Ability to approach problems in a logical, systematic way, and to break down complex issues into manageable parts.

• Problem-Solving Skills: The ability to identify, analyze, and devise solutions for complex problems

• Project Management: Ability to plan, organize, and manage resources to bring about the successful completion of specific project goals and objectives.

• Communication Skills: Excellent written and verbal communication skills, and the ability to explain complex data concepts to non-technical stakeholders.

• Change Management: Knowledge of and ability to apply change management procedures, ensuring smooth transitions when implementing new data systems or processes.

• SLA Management: Ability to define, track, and ensure compliance with Service Level Agreements.

Attitudes

• Innovative Mind-set: Displays a willingness to challenge the status quo and continuously seeks to implement new and improved data technologies and strategies.
• Result-Oriented: Focuses on achieving goals and delivering on commitments, with a high emphasis on the quality of work and the value delivered for the bank.
• Collaborative: Enjoys working in a team-oriented environment, and understands the importance of sharing knowledge and best practices.
• Adaptive: Shows the ability to adjust quickly to new situations and changing priorities, remaining flexible and efficient in the face of challenges.
• Curious: Maintains an interest in the ever-evolving field of data engineering, eager to learn about new data technologies and practices.
• Ethical: Upholds strong professional ethics, with a focus on data privacy and security regulations.
• Proactive: Takes initiative to anticipate needs, identify potential issues, and propose effective solutions.
• Patient: Understands that data-related tasks can be complex and time-consuming, and does not rush processes at the expense of quality.
• Service-Minded: Keeps the needs of both internal and external customers in mind, and works towards improving their experiences.
• Critical Thinker: Always questioning, never taking data at face value, and using analytical abilities to understand the context behind the numbers.
• Humility: Willingness to admit mistakes, learn from others, and seek help when necessary.
• Pragmatic: practical and hands-on approach to designing, building and maintaining data infrastructure and solutions

If you are interested, please send your CV to marcel@kazarmaconsulting.eu