• Design and develop data pipelines and transformation workflows using DBT. • Build and optimize data models within Snowflake to support analytics and reporting. • Support the migration of orchestration and transformation processes from Dataiku to DBT. • Develop integrations between enterprise systems including CRM platforms and analytics environments. • Ensure data quality, consistency, and reliability across the data platform. • Implement and maintain data transformation best practices and coding standards. • Produce clear technical documentation for pipelines, models, and processes. • Collaborate with BI engineers, PM/BA, and Data Architects on platform design and improvements. • Proactively identify performance issues and optimize data pipelines.

• Design, develop, and maintain scalable data pipelines using Microsoft Azure and Databricks (Spark). • Build and optimize ETL/ELT workflows in Databricks, leveraging PySpark and SQL for large-scale data processing. • Integrate and land various data sources—including internal, licensed third-party, and public datasets—into the global data lake. • Develop and maintain Delta Lake architectures to ensure reliable, high-performance data storage and processing. • Collaborate with business analysts, data scientists, and engineering teams to define data requirements and deliver high-quality data products. • Use Python and SQL to automate data ingestion, transformation, and validation processes. • Monitor, troubleshoot, and optimize Databricks jobs and clusters for performance, scalability, and cost efficiency. • Implement data quality, governance, and security best practices across the platform. • Participate in documentation, code reviews, and knowledge sharing across a global team.

• Own the vision and technical strategy for data engineering and analytics engineering at Philo, setting the direction for how data is ingested, modeled, and delivered across the organization • Own the team’s roadmap, balancing strategic long-term investments with near-term stakeholder needs • Lead and grow the Data Engineering team, including hiring, career development, performance management, and supporting the team in generally thriving • Lead the design and implementation of Philo's data platform architecture, including setting technical standards and best practices for data engineering and analytics engineering work • Collaborate with cross-functional teams to understand data platform needs and translate business requirements into technical solutions • Build and maintain stakeholder trust in data and reporting — ensuring that business teams have reliable, well-documented, and well-understood data products • Design scalable data pipelines and infrastructure; drive technical decisions with a focus on long-term sustainability • Implement data governance, security, and quality control processes as needed • Ultimately, deliver on the alchemy of transforming raw, messy application data into trusted business insight through scalable dimensional modeling and a metric layer that faithfully reflects product and customer behavior.

• Assess the current data and BI architecture • Organize and standardize datasets within the Data Lake • Define standardized data models • Implement data governance best practices • Build data ingestion and transformation pipelines • Structure analytical layers (raw, curated, analytics) • Improve BI query performance and quality • Work with business teams to ensure consistency of metrics • Document data structures and models used • Support the evolution of the company's data architecture