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Tourism Economics, a subsidiary of Oxford Economics, is looking to hire an Analytics Engineer based in our Wayne, PA office. As an Analytics Engineer, you will serve as the bridge between data engineering, BI, and product while designing scalable data models, building reusable analytical frameworks, and developing the semantic layer that standardizes metrics across the organization. Your work ensures that every insight, whether surfaced in pipelines, models, or dashboards, is accurate, aligned, and trusted. You will work closely with the Lead Engineer to define modeling logic, metric definitions, and analytical foundations for new product capabilities. You will translate business requirements into technical solutions. You approach data architecture with thoughtfulness and clarity, maintaining a balance between reliability, performance, and simplicity.
Job Responsibility:
Build and own the data modeling layer in Snowflake using dbt, transforming raw datasets into reliable, well-documented tables and metrics
Design scalable, future-proof data structures that support new product features, analytical use cases, and organizational growth
Create and maintain the semantic layer that standardizes metrics and enables self-service analytics
Automate, monitor, and optimize data pipelines to ensure reliability, version control, and transparent data lineage
Partner with BI engineers to deliver intuitive, performant datasets for dashboards and reporting in tools like Looker, Tableau, or Power BI
Work with business stakeholders to gather requirements and translate them into effective, technically sound data solutions
Collaborate with data, product, and analytics teams to identify opportunities for data-driven decision-making
Implement and maintain QA, testing, and data validation standards across all data models
Develop measurement and evaluation frameworks to ensure models meet performance and accuracy expectations
Maintain comprehensive documentation of data models, business logic, and metric definitions
Ensure data assets are discoverable and easy for analysts and stakeholders to use independently
Champion best practices and act as a force multiplier through clarity, consistency, and strong data design principles
Communicate technical concepts clearly and translate them into actionable insights for non-technical stakeholders
Requirements:
3–6 years as an analytics engineer, BI engineer, or data engineer in a data-driven SaaS or analytics-heavy environment
Solid understanding of data modeling principles (Kimball, Data Vault, or modern ELT patterns)
Strong proficiency in SQL and dbt (required)
Experience with modern cloud data warehouses (Snowflake, BigQuery, or Redshift)
Familiarity with orchestration tools such as Airflow, Prefect, or Dagster
Working knowledge of Python for transformation, validation, or automation
Experience with BI tools such as Looker, Tableau, or Power BI
Nice to have:
Thoughtful approach to algorithmic and model design—evaluating trade-offs and building solutions that scale cleanly
Strong instincts for data product quality, balancing speed, reliability, and reproducibility
Business acumen to translate organizational objectives into robust data solutions
Excellent communication skills with the ability to make technical concepts accessible to non-technical stakeholders
Proven success working cross-functionally with business, product, and technical teams