Senior Analytics Engineer
Description
Are you excited about building analytics foundations that people can truly rely on, turning complex business questions into trusted datasets, metrics, and reporting experiences? At Super Technologies, data is a strategic enabler of decision-making, experimentation, and performance measurement. Our Analytics Platform team builds the foundations that make data reliable, accessible, and actionable, partnering with product, commercial, and engineering teams to measure what matters. You’ll be joining a mature and growing community of 40+ data engineers and analytics engineers who are shaping one of the most advanced data ecosystems in the industry.
Responsibilities
- Design and build curated analytical datasets, metric implementation, and semantic models that enable consistent self-service analytics across the company
- Work hands-on with data warehouse modelling to turn evolving product and business needs into scalable, reusable data structures
- Partner closely with product, data scientists and business stakeholders to clarify requirements and translate them into reliable data models and reporting-ready outputs
- Build and own production dashboards (Tableau) on top of curated datasets and metric definitions, ensuring correctness, performance, and a consistent “single source of truth”
- Contribute to data quality, observability, lineage, and documentation practices to increase trust and reduce firefighting
- Promote reuse over reinvention, identify ad-hoc logic in reporting views or custom SQL and refactor it into curated, high-quality models that multiple teams can rely on.
- Raise the engineering bar through reviews, testing, automation, and pragmatic architectural improvements
- Apply engineering rigor to analytics - treat semantics as code utilizing version control, testing, automation, and CI/CD practices to manage the lifecycle of our business logic.
- Drive self-service adoption - create the foundations that allow analysts and business users to explore data safely and independently, without needing to reinvent the wheel.
- Work embedded within Data Engineering squads, collaborating daily with both data engineers and analysts on shared foundations and end-to-end delivery
Requirements
- Strong SQL skills and proven experience building analytical datasets and metrics in a modern data warehouse (Snowflake preferred, BigQuery, Redshift and similar also valuable)
- Solid understanding of data warehouse modeling and best practices (dimensional modeling, fact/dimension design, grains, slowly changing dimensions, semantic consistency)
- Experience working with reporting and BI tools (Tableau preferred), including understanding how data sources, extracts, and dashboard logic impact correctness, performance, and trust
- A production mindset: you care about reliability, maintainability, documentation, and operational ownership, not just creating a dataset once
- Strong ownership and collaboration skills, able to drive clarity in ambiguous problem spaces and partner effectively with both technical and non-technical teams
- Excellent communication skills with a pragmatic, problem-solving approach
- Bonus points for: Experience with orchestration and workflow tooling (Airflow or similar)
- Experience with data cataloging, lineage, and governance tooling (e.g., DataHub)
- Background in product-led, experimentation-driven, or high-growth environments where definitions evolve quickly
- Experience supporting downstream operational consumers (e.g., CRM/audience platforms) or ML/feature engineering use cases
- Familiarity with streaming/event-heavy ecosystems (Kafka or similar)
