Senior Analytics Engineer

Kilo•Capon Bridge, WV

7d•€4,200 - €6,000•Hybrid

About The Position

The analytics team builds and maintains the analytics engineering layer that powers reporting, experimentation, and decision-making across the company. The team focuses on creating clean, scalable data models, well-defined metrics, and reliable tracking foundations using dbt and BigQuery. This role involves building and maintaining a scalable analytics engineering layer in dbt, improving tracking and instrumentation, implementing data quality measures, building data products, and continuously improving workflows. The role also requires clear communication with cross-functional teams and stakeholders, and supporting team members in data warehousing, reporting, and analytics solutions.

Requirements

3+ years of experience in analytics engineering, BI engineering, or data engineering, with a strong focus on data modeling
Strong SQL skills and use Python for analytics workflows and automation
Hands-on experience with dbt, BigQuery, Looker, and CI/CD practices
Strong problem-solving and critical thinking skills: able to challenge unclear requests, design robust metrics, and communicate trade-offs
Experience setting up or scaling data quality practices
Able to work with stakeholders, write clear documentation, and align teams on definitions
A curious, growth-oriented mindset, and be comfortable learning new tools and approaches
Fluent in English, with the ability to explain technical and business issues clearly

Nice To Haves

Experience with marketing and website tracking concepts (events, attribution, funnels)

Responsibilities

Build and maintain a scalable analytics engineering layer in dbt, including clean models, marts, shared dimensions, and metric-ready tables
Improve tracking and instrumentation across domains, and identify new opportunities where data can add value.
Implement and monitor data quality to ensure accuracy, completeness, and consistency
Build data products when needed (e.g., marketing performance datasets, customer lifecycle models, cohorting, experimentation-ready tables)
Continuously improve workflows with best practices (naming conventions, data modeling standards, data quality testing, templates, and CI/CD)
Communicate clearly with cross-functional teams and non-technical stakeholders
Support team members in designing, developing, and implementing data warehousing, reporting, and analytics solutions