Responsibilities
- Build and maintain data pipelines and integrations using SQL, Python, DBT and Snowflake. Bring together operational, CRM and third-party data, and create reusable models that support analytics and business workflows.
- Develop semantic models and views with clear metric definitions, entity relationships, business rules and verified example queries. Make the same trusted definitions usable across agents, applications and reporting.
- Build and improve AI agents, copilots and automated workflows that combine structured data, documents and business tools. Work on use cases such as client intelligence, call preparation, customer support and sales productivity.
- Partner directly with business teams to understand their processes, question assumptions and identify where better data or automation will help. Turn ambiguous requests into practical solutions and support them after launch.
- Use AI coding assistants and development tools in your own work, and help the Data team develop repeatable practices for using them well. Review generated code, validate results and share what works.
- Help technical and business colleagues use AI tools effectively through hands-on sessions, documentation, reusable examples and coaching. Use their feedback to improve the tools and workflows you build.
- Build quality and reliability into the full solution, including data tests, monitoring, agent evaluations, version control and deployment practices. Work with Engineering and Security on appropriate access, permissions and human review.
- Measure whether solutions are useful in practice through sustained adoption, output quality, time saved and improvements in business workflows. Balance new development with maintenance, performance and cost.
What Success Looks Like
The solutions you build become part of how teams at Pontera work every day. You partner with business stakeholders to define the problem and what improvement would look like, then deliver tools that save time, improve decisions or increase the team’s capacity. You can explain what changed and show evidence of the value created.
Your data pipelines and semantic models provide a dependable foundation for analytics, agents and automated workflows. Business definitions are clear, outputs are tested, and teams can trust the answers they receive. New use cases build on reusable models and components, making each subsequent solution easier to deliver and maintain.
Business teams become more independent in how they use data. They can explore questions, understand the answers and take informed action within their permissions. Recurring questions and manual requests increasingly become opportunities for better self-service, giving the Data team more time for deeper problems and new capabilities.
The agents and workflows you deliver work reliably beyond the initial launch. They have appropriate evaluation, monitoring, documentation and access controls, with clear handling of failures and situations that require human review. You use feedback and observed usage to improve quality, usefulness and cost over time.
You raise the AI capabilities of the people around you. Through practical guidance, shared examples and collaboration, colleagues become more confident using AI, evaluating its output and applying it to their own work. Within the Data team, the approaches you develop help others build faster while maintaining sound engineering judgment.
You take ownership from discovery through ongoing improvement. You identify opportunities, make thoughtful tradeoffs, communicate progress and surface risks early. Your work is documented and understandable to others, and you actively share knowledge, support teammates and contribute to an environment where different perspectives improve the decisions we make.