Senior Data Architect

Roles and responsibilities

• Design, architect, and implement a scalable data platform for the business unit’s specific needs that supports data-driven decision-making, operations efficiency, and democratization of data
• Design a data platform architecture that promotes seamless integrations with 3rd party applications, Corporate IT systems including ERP, and in-house built solutions
• Act as the primary liaison between the business unit technology team and Corporate IT, ensuring that data architecture, governance standards, cyber security and compliance protocols are maintained
• Establish process to ensure federation of corporate IT policies within the business unit
• Lead and mentor a team of data engineers to build all necessary data pipelines and infrastructures inspired by latest best practices in modular design and microservices.
• Collaborate with business stakeholders, product managers, software engineers, and other data engineers to ensure the data platform supports the innovation ambitions of the business unit including Data Science, Machine Learning, and Artificial Intelligence
• Develop and implement logical and physical data models that meet the unique needs of the business unit while aligning with Corporate IT standards for data architecture.
• Develop and oversee data ETL processes guaranteeing optimized performance
• Maintain detailed documentation of the data architecture, data flows, and integrations to ensure transparency and consistency across both business unit and corporate data systems.

Qualifications:

• Bachelor’s degree in Computer Science, Computer Engineering, Data Science or a related field
• A Master in the field is a plus
• 8 years experience in data architecture, data engineering, with significant experience in enterprise data design.

Desired candidate profile

1. Data Architecture Design

• Architecting Data Solutions: Design and implement scalable and high-performance data architectures that meet both current and future business needs.
• Data Modeling: Develop data models (conceptual, logical, and physical) to structure and organize data in a way that maximizes its value while ensuring data integrity and efficiency.
• Big Data Solutions: Design and implement data architectures to handle large volumes of data, including the use of NoSQL databases (e.g., Cassandra, MongoDB) and distributed systems (e.g., Hadoop, Spark).
• Cloud Architecture: Create and manage cloud-based data architectures using platforms like AWS, Google Cloud, or Azure.
• Data Warehousing: Lead the design and implementation of data warehouses, data lakes, and ETL (Extract, Transform, Load) pipelines to consolidate data from multiple sources.
• Data Integration: Design and implement systems for data integration, ensuring data flows seamlessly between systems and is harmonized for analysis.

2. Data Governance and Security

• Data Governance: Establish policies and frameworks for managing data quality, consistency, and availability across the organization.
• Data Security: Ensure that data is securely stored and processed, adhering to privacy regulations (e.g., GDPR, CCPA) and implementing encryption, access control, and auditing measures.
• Compliance: Ensure that data architecture complies with industry standards, security practices, and regulatory requirements.
• Metadata Management: Implement and manage metadata systems to track and govern the usage, lineage, and transformation of data.

3. Collaboration and Leadership

• Cross-Department Collaboration: Work closely with teams such as data engineering, business intelligence, data science, and product management to define data needs and ensure alignment across the organization.
• Mentoring: Provide leadership and mentorship to junior data architects, data engineers, and other technical teams to ensure the adoption of best practices.
• Stakeholder Communication: Present and explain technical concepts to non-technical stakeholders, including senior management and business leaders, helping them understand the strategic value of data architecture.
• Project Management: Lead or participate in the planning and execution of data-related projects, ensuring they are delivered on time and meet business goals.

4. Optimization and Performance Tuning

• Data Processing Optimization: Continuously assess and improve the performance of data systems, including optimizing queries, data pipelines, and storage solutions.
• Scalability: Ensure that data solutions are scalable to handle growing data volumes and changing business needs.
• Disaster Recovery: Design and implement disaster recovery strategies to ensure that data is protected and can be quickly restored in case of a failure.

5. Innovation and Strategy

• Evaluate New Technologies: Stay up-to-date with the latest trends and technologies in data architecture, including AI/ML, data lakes, real-time analytics, and cloud platforms.
• Long-Term Strategy: Define and implement the long-term data strategy, ensuring that the architecture aligns with the evolving needs of the business and technological advancements.
• Emerging Trends: Explore and incorporate new technologies, tools, and best practices to improve data architecture, such as data mesh, data fabric, and serverless architectures.