Senior Data Engineer, Analytics

Job description

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