About this role
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 JR00000329