Senior Analytics Engineer

super; United States; Remote; USD 110,000 - 160,000; Full time

Benefits offered by this job

Medical insurance

Open annual leave

Training and development

Job summary

super is seeking a data analytics engineer to design and build curated analytical datasets, metric implementations, and semantic models that enable self-service analytics across a sports, gaming, and fan-experiences tech platform. You will collaborate with product, data science, and commercial teams to turn evolving business needs into robust data structures.

In this role you will own production Tableau dashboards and contribute to data quality, lineage, and documentation, while applying

Qualifications

• Experience building analytical datasets and metrics in modern data warehouses.
• Strong SQL and data modelling fundamentals for scalable reports.
• Excellent communication and cross-functional collaboration skills.

Responsibilities

• Design and build curated analytical datasets and semantic models for self-service analytics.
• Develop scalable data warehouse models from product and commercial needs.
• Partner with product, data science, and business teams to translate requirements.
• Create and own Tableau dashboards on curated data with trusted sources.
• Improve data quality, observability, and documentation.
• Refactor ad-hoc logic into reusable models used by multiple teams.
• Treat semantics as code with version control and CI/CD.

Skills

SQL

Tableau

Data warehouse

Dimensional modelling

Ownership

Communication

Tools

Snowflake

BigQuery

Redshift

DataHub

Job description

Role overview

Design and build curated analytical datasets, metric implementations, and semantic models that enable consistent self-service analytics across a sports, gaming, and fan-experiences technology platform. The role sits within a mature analytics community and partners closely with product, data science, and commercial stakeholders to turn evolving business needs into reliable, reusable data structures.

Responsibilities

• Design and build curated analytical datasets, metric implementations, and semantic models that support consistent self-service analytics across the business.
• Turn evolving product and commercial needs into scalable, reusable data warehouse models through hands‑on dimensional modelling.
• Partner 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 Tableau dashboards on top of curated datasets and metric definitions, ensuring correctness, performance, and a consistent single source of truth.
• Strengthen data quality, observability, lineage, and documentation practices to increase trust and reduce firefighting.
• Identify ad‑hoc logic in reporting views or custom SQL and refactor it into curated, high-quality models that multiple teams can rely on.
• Treat semantics as code using version control, testing, automation, and CI/CD to manage the lifecycle of business logic.

Requirements

• Strong SQL skills with proven experience building analytical datasets and metrics in a modern data warehouse such as Snowflake, BigQuery, or Redshift.
• Solid understanding of data warehouse modelling best practices, including dimensional modelling, fact/dimension design, grains, slowly changing dimensions, and semantic consistency.
• Experience with reporting and BI tools such as Tableau, including how data sources, extracts, and dashboard logic impact correctness, performance, and trust.
• A production mindset with care for reliability, maintainability, documentation, and operational ownership beyond one‑off dataset creation.
• Strong ownership and collaboration skills with the ability to drive clarity in ambiguous problem spaces and partner with both technical and non‑technical teams.
• Excellent communication skills and a pragmatic, problem‑solving approach.

Nice to have

• Experience with orchestration and workflow tooling such as Airflow or similar.
• Experience with data cataloguing, lineage, and governance tooling such as DataHub.
• Background in product‑led, experimentation‑driven, or high‑growth environments where definitions evolve quickly.
• Experience supporting downstream operational consumers such as CRM or audience platforms, or ML and feature engineering use cases.
• Familiarity with streaming or event‑heavy ecosystems such as Kafka.

Benefits and work setup

• Medical or health insurance, open annual leave, employee assistance programme, and training and learning development support, with additional benefits varying by country and shared during the hiring process.