Staff Analytics Engineer

The Role

As a Staff Analytics Engineer in Ascenda’s Data Team, you will act as a

Data Architect

and

technical leader,

designing, building, and governing our

data medallion architecture

to deliver reliable, scalable, and high-trust data products — serving both internal and external stakeholders.

You will shape how our

loyalty-as-a-service

business models its data, metrics, and data products for enterprise clients and partners.

You’ll collaborate closely with product managers, commercial account managers, engineers, and data scientists in a modern, fast-moving environment that values

autonomy, clarity, and measurable outcomes.

Your Impact

• Architect the data foundations that enable both internal teams and global partners to turn raw data into trusted insights powering real business decisions.
• Champion dimensional modelling principles, ensuring our medallion architecture scales efficiently with increasing data volume, variety, and complexity.
• Design, standardise, and evolve our data modelling (dbt) frameworks to transform raw data into self-explanatory, performant, and reusable datasets for analytics and reporting.
• Empower analysts, engineers, and business teams with high-quality semantic layers and data models that serve as a single source of truth across Ascenda and our partner ecosystem.
• Drive excellence in our modern data stack — AWS Redshift, dbt, Airflow, Meltano, Python (Pandas, Jupyter) — ensuring performance, scalability, and maintainability.
• Mentor analytics engineers, define best practices, and embed data quality, governance, and observability throughout our data stack.
• Work closely with data engineers, scientists, and AI engineers to power machine learning workflows and predictive models.

Who You Are

• 7–10 years of experience in analytics engineering, data engineering, or data modelling within large-scale data environments (fintech, banking, e-commerce, telco, etc.).
• Deep hands-on expertise with dbt, applying best practices in modular design, macros, reusable patterns, and unit testing.
• Proven experience designing and evolving enterprise-scale data models (Kimball, Inmon, or Data Vault).
• Strong SQL and Python skills, with a performance-driven mindset and production-ready practices (CI/CD, version control, monitoring).
• Strong analytical and problem-solving skills, with the ability to draw insights and make data-driven recommendations.
• Excellent business acumen — able to translate product and business stakeholder needs into scalable data models and semantic layers.
• Outstanding communicator who can articulate architectural trade-offs and influence both technical and non-technical audiences.
• Collaborative, proactive, and self-motivated, with a commitment to continuous learning and improvement.
• Keeps up-to-date with emerging data and AI technologies, actively exploring innovative applications such as GenAI for analytics and data automation.
• Strong alignment with Ascenda’s core values of growth mindset, hands-on ownership, supportive collaboration, and radical simplicity