Data Engineering Lead Architect

The Data Engineering Lead Architect is a key contributor in our growing Data organization, responsible for guiding the design, development, and strategic delivery of data solutions. This role oversees the execution of cloud-based data architectures, ensuring the platform is scalable, secure, and high-performing while meeting both business and technical needs. We are looking for a hands-on leader with strong technical depth who can also drive broader strategic initiatives to successful completion.

• Collaborate with the Product Owner to define, refine, and prioritize technical user stories and enablers within the product backlog.
• Partner with the Scrum Master and Product Owner to address, escalate, and resolve technical blockers.
• Actively participate in key agile ceremonies, including backlog refinement, sprint planning, daily standups, iteration reviews, and retrospectives.
• Provide daily technical direction and development support to the teamguiding solution design, assisting with code fixes, and supporting technical release planning.
• Deliver committed user stories from the sprint backlog to meet iteration objectives.
• Develop and maintain technical documentation, including design specifications, technical roadmaps, and architecture references.
• Collaborate with subject-matter experts across the organization to support user story development and solution design.
• Lead the design, development, and implementation of data solutions leveraging AWS core data services, while driving innovation in Data Engineering, Governance, Integration, and Virtualization.
• Oversee all technical components of data solutions, ensuring seamless delivery from proof-of-concept through production deployment.
• Continuously optimize the data platform to improve performance, resiliency, scalability, and security, while integrating new technologies and best practices.
• Work closely with business stakeholders, data architects, and cross-functional teams to translate complex business needs into effective technical solutions.
• Define and execute data management strategies, including Data Warehousing, Master Data Management, and Advanced Analytics initiatives.
• Balance strategic oversight with hands-on technical workcontributing to architecture, development, and deployment of key data systems as required.
• Monitor system performance, identify potential risks or issues, and implement proactive or corrective measures to ensure platform stability.
• Ensure the accuracy, consistency, and overall quality of data across applications, analytics, and operational processes.
  
  
  

• Bachelors degree in a computer-related field such as Information Technology, Computer Science, or Management courses with a specialization in computer studies
• Minimum of 12 years of extensive hands-on experience in developing and delivering data solutions, with a strong background in the AWS Cloud Platform
• Proven expertise in designing and implementing AWS data services including S3, Redshift, Athena, Glue, Python, and PySpark, with solid knowledge of data architecture and design patterns
• Strong experience in building large-scale data ecosystems such as Data Lakehouse, Data Warehousing, Master Data Management, and Advanced Analytics platforms
• Demonstrated ability to manage multiple projects in fast-paced, high-pressure environments while ensuring timely and high-quality delivery
• Strong analytical and problem-solving abilities with a data-driven approach to challenges
• Proficiency in coding for data solutions to enable efficient data access, integration, and support for analytics and decision-making
• Excellent written and verbal communication skills with the ability to work independently or collaboratively and provide structure in ambiguous situations
• Ability to clearly communicate complex technical topics to both technical and non-technical stakeholders
• Experience working with cross-functional teams and aligning technical data strategies with broader business goals
• Full lifecycle development experience, including successful implementation of an enterprise-wide Data Warehouse using AWS Cloud Platform technologies such as AWS DMS, Glue, Python, PySpark, and Redshift