About the Role: 

Ryt Bank is seeking a highly motivated and enthusiastic a Senior Data Engineer, Analytics to bridge the gap between data engineering and analytics, focusing on building and maintaining scalable data models while enabling stakeholders to make data-driven decisions. You will be operating with a high level of autonomy, collaborating directly with product, business partners, and peer engineering teams to align on requirements and execute effectively in a fast-paced environment. You will work closely with experienced professionals, contribute to real-world projects, and develop essential skills for a successful career in the data engineering team.

Our engineering team thrives on collaboration, innovation, and a shared commitment to excellence. You will also have the opportunity to implement robust data, mentor junior engineers, and contribute to a culture of continuous learning and technical excellence.

If you're a passionate analytics engineer eager to make a difference, join us as we shape the future of technology together.

Key Responsibilities: 

• Design, implement and maintain robust data models to support analytics, dashboards, and self-serve tools
• Architect and develop high quality data assets for business, analytics and regulatory reporting use cases
• Collaborate with stakeholders to understand business requirements and translate them to technical solutions
• Establish data modelling best practices, tooling, documentation and testing methodologies - ensuring models are highly maintainable and scales with complexity
• Lead technical design discussions and contribute to data architecture decisions - spotting opportunities to reduce complexity and cost
• Lead, guide and mentor team members on best practices - You will review the designs and work of engineers on the team and set a high bar for quality.

Qualifications: 

• Strong passion for data modelling - SQL and data modelling are second nature to you
• Proven experience with dbt and modern data warehouse platforms (Snowflake, Redshift) - you are comfortable with general warehousing concepts
• Strong understanding of software engineering best practices and data engineering principles
• Experience implementing data quality monitoring and testing frameworks
• Ability to tackle complex problems from both technical and business perspectives
• Excellence in stakeholder communication and leading technical initiatives in a fast-paced environment
• Background in implementing metrics frameworks and data governance is a bonus
• Familiarity with AI/ML and their applications in analytics is a bonus
• Ability to think strategically about banking products / operations and how our underlying data models will unlock more insights and value for our customers is a bonus

Technology Stack We Use:

• Language: SQL, Python
• Orchestration: Airflow
• Transformation: dbt
• Warehousing: Greenplum
• Deployment: Docker, Kubernetes

Impact & Growth Opportunities:

• Lead critical data modelling initiatives that power company-wide analytics
• Shape data engineering practices and tooling decisions
• Mentor and grow other team members' technical capabilities
• Drive adoption of modern data solutions
• Influence data architecture and governance strategies