The Analytics Engineering Technical Lead will define and own the modeling architecture, standards, and technical direction within RSI’s cloud-native Data Platform. This role is accountable for ensuring that analytics-ready data is reliable, scalable, and aligned to long-term business needs across growth, operations, and risk.

You will lead the design and governance of core data models in Snowflake and dbt, establishing best practices that enable high-quality, self-service analytics without sacrificing trust or performance. As a hands-on technical leader, you will set standards for modeling, testing, documentation, and deployment while mentoring other Analytics Engineers and raising the overall technical bar of the team.

This role partners closely with Data Engineering, Analytics, and business stakeholders to translate evolving requirements into durable data products. You will help balance speed and governance, prioritize platform investments, and ensure our data foundation scales with the business.

Our modern data stack includes Snowflake, AWS, Python, Airflow, and dbt.

What You'll Do:

• Lead the design, development, and lifecycle management of curated datasets, dbt models, and analytics data marts aligned to business domains and metrics.
• Establish and evolve standards and best practices for dbt modeling, testing, documentation, and code organization to support long-term scalability.
• Ensure analytical datasets are accurate, reliable, and trusted, serving as a single source of truth for business, product, and stakeholders.
• Embed data quality checks, documentation, and governance directly into analytics pipelines.
• Partner closely with Product, Data Engineering and Analytics teams to translate business requirements into well-modeled, performant data products.
• Mentor and support Analytics Engineers through technical guidance, design reviews, and knowledge sharing.
• Continuously improve and modernize analytics workflows by introducing automation, tooling enhancements, and AI-assisted development where appropriate.
• This role includes technical leadership and mentoring responsibilities for the team, including other Analytics Engineers.

What You'll Bring:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related quantitative discipline.
• Technical Leadership: Proven experience defining technical direction, standards, and architectural decisions within analytics engineering or curation teams.
• Data Modeling Expertise: Deep understanding of dbt, data marts, dimensional modeling, and self-service BI enablement.
• Data Engineering Background: 7+ years of experience in data warehousing or development; 5+ years of experience in hands-on data engineering using SQL and Python.
• Cloud Infrastructure: Proficiency with cloud platforms (AWS, GCP, or Azure), including containerization (Docker/Kubernetes).
• Tooling & Orchestration: Experience with tools like dbt, Airflow, S3, REST APIs, and large-scale DWHs (Snowflake, Redshift, BigQuery, etc.).
• Change Management: Skilled at leading cross-functional data initiatives and driving adoption of new tools and standards.
• People Leadership: Experience mentoring and guiding distributed teams across time zones.
• Communication: Strong written and verbal English skills with the ability to collaborate across technical and business functions.
• Able to travel occasionally both domestically and internationally #LI-HYBRID