Data Engineering Tech Lead

Job description

• Data Tech Lead plays a pivotal role within our evolving Data organization, leading the solution design, development, and strategic implementation of data solutions. The candidate will be responsible for driving execution of cloud-based data architectures, ensuring the platform's scalability, security, and performance while addressing both business and technical requirements. This role demands a hands-on leader with deep technical expertise who can also steer strategic initiatives to success.

Preferred skills

• Strong problem-solving skills, with the ability to make data-driven decisions and approach challenges methodically.
• Proficiency in data solution coding, ensuring access and integration for analytical and decision-making processes.
• Good verbal & written communication skills and ability to work independently as well as in a team environment providing structure in ambiguous situation.
• Ability to effectively communicate complex technical solutions to both technical and non-technical stakeholders.
• Experience working with multi-disciplinary teams and aligning data strategies with business objectives.
• Must have full scale development experienced and able to successfully implemented an enterprise wide Data Warehouse using AWS Cloud Platform, AWS DMS, Glue, Python, PySpark, Reddshift

Qualifications

• Must have a Bachelor of Science of computer related courses, eg. Information Technology, Computer Science, Management courses with major in computer studies.
• At least 12 years of strong hands-on experience in developing and delivering Data Solutions, with a strong background in AWS Cloud Platform.
• Proven experience in designing and implementing AWS data services (such as S3, Redshift, Athena, Glue, Python, PySpark.) and a solid understanding of data service design patterns.
• Expertise in building large-scale data platforms, including Data Lakehouse, Data Warehouse, Master Data Management, and Advanced Analytics systems.
• Demonstrated experience managing multiple projects in a high-pressure environment, ensuring timely and high-quality delivery.

Responsibilities

• Collaborate with the Product Owner in the writing and prioritization of technical user stories and enablers in the product backlog.
• Work with Scrum Master and Product Owner to resolve and escalate technical impediments.
• Participate in team-level agile ceremonies, like backlog refinement, sprint planning, daily standups, iteration demos and retrospectives.
• Provide technical and development guidance to team members daily (e.g. set direction on technical solutioning, support code fixes, technical release planning, etc.)
• 'Burn' user stories from the sprint backlog to deliver on the team's iteration goals
• Create and maintain technical documentation, such as design documents, technical roadmaps, etc.
• Engage with subject-matter experts across the organization when they are required for user story development
• Spearhead the solution design, development, and delivery of data solutions using AWS core data services, driving innovation in Data Engineering, Governance, Integration, and Virtualization.
• Oversee all technical aspects of data systems, ensuring end-to-end delivery from proof-of-concept (PoC) to production deployment.
• Continuously enhance the data platform delivery efficiency to improve performance, resiliency, scalability, and security while incorporating new data technologies and methodologies.
• Work closely with business partners, data architects, and cross-functional teams to translate complex business requirements into technical solutions.
• Develop and implement data management strategies, like, Data Warehousing, Master Data Management, and Advanced Analytics solutions.
• Combine technical solutioning with hands-on work as needed, actively contributing to the architecture, coding, and deploying critical data systems.
• Ensure system health by monitoring platform performance, identifying potential issues, and taking preventive or corrective measures as needed.
• Be accountable for the accuracy, consistency, and overall quality of the data used in various applications and analytical processes.