Are you excited about building modern data infrastructure that powers self-service analytics and high-quality data products at scale? 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 closely with product, analysts, data scientists, and engineering teams to measure what matters.

You'll be joining a mature and growing community of 40+ data engineers who are shaping one of the most advanced data ecosystems in the industry.

What you'll be doing:

• Design, build, and evolve scalable ETL/ELT pipelines for high-volume, fast-changing datasets
• Own medium-to-large data initiatives end-to-end, from requirement shaping and technical design to production rollout and ongoing improvement
• Model reliable, analytics-ready data using proven warehouse methodologies such as Kimball, Inmon, or similar approaches
• Develop and maintain curated analytical layers, semantic models, and data marts that enable self-service and trusted decision-making
• Orchestrate robust workflows using tools such as Apache Airflow and improve reliability, observability, and maintainability of data pipelines
• Partner with software engineers, analysts, product managers, and business stakeholders to translate ambiguous requirements into scalable data solutions
• Proactively identify data quality, performance, and modeling issues and drive improvements before they become business problems
• Contribute to architecture and engineering decisions within your domain, balancing speed, quality, and long-term maintainability
• Mentor less senior data engineers through code reviews, design discussions, documentation, and day-to-day guidance
• Raise the engineering bar through testing, automation, CI/CD practices, clear documentation, and thoughtful technical standards

We're looking for:

• Strong SQL and Python skills, with solid hands-on experience building production-grade data pipelines and analytical models
• Deep understanding of dimensional modeling methodologies and modern data warehouse design in platforms such as Snowflake, BigQuery, or similar
• Experience owning projects independently and driving delivery across multiple stakeholders or teams
• Solid experience with orchestration tooling such as Airflow, Dagster, or similar
• Strong understanding of data quality, lineage, observability, and operational excellence in a data engineering environment
• Ability to take ambiguous business problems and turn them into clean, maintainable, well-documented data solutions
• Experience working with cloud platforms, preferably AWS, though Azure or GCP experience is also valuable
• Confidence influencing technical decisions within your team and collaborating effectively with both technical and non-technical stakeholders
• Strong communication skills and a collaborative, problem-solving mindset

Bonus points for:

• Experience with event-driven source systems
• Experience with cloud-native analytical databases like Snowflake, BigQuery, Databricks
• Familiarity with semantic layers, metrics modeling, and enabling data products beyond traditional BI reporting
• Exposure to experimentation-driven or product-led environments
• Experience with data governance, cataloging, and documentation practices such as DataHub
• Experience improving developer productivity through tooling, shared frameworks, or reusable patterns
• Multi-cloud exposure and understanding of platform trade-offs