Company
Technology
SeniorDataAnalyticsEngineer-DataInsights
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Analytics Engineer - Data Insights. Skills: Data modeling, Analytics engineering, Data warehousing. Design data models. Build data models”
Industry & Context.
Analytical skills; Problem-solving skills
Ownership, Autonomy
What They're Looking For.
Must Have
5+ years of experience, Expertise in SQL, 3+ years of experience with Python, 3+ years of experience with dbt
Nice to Have
Familiarity with cloud platforms, Familiarity with source control tools, Experience with semi-structured data formats, Experience with BI tools, PhD preferred
What You'll Do.
Develop business metrics
Standardize business dimensions
Understand decision-making needs
Translate needs into data products
Implement data transformations
Optimize data transformations
Ensure maintainability
Build semantic layers
Evolve KPI frameworks
Define best practices
Collaborate with data engineering
Collaborate with analytics teams
Collaborate with business teams
Support end-to-end delivery
How You'll Work.
Team & Collaboration
Cross-functional stakeholders; Cross-functional environments; Data engineering teams; Analytics teams; Business teams
Communication Scope
Explain technical concepts
Process & Methodology
Manage complex data projects
Full Job Description
## Accountabilities Design, build, and maintain scalable data models, transformations, and pipelines using modern analytics engineering practices in cloud data warehouse environments. Develop and standardize core business metrics and dimensions to enable consistent, self-service analytics across the organization. Partner with cross-functional stakeholders to understand decision-making needs and translate them into reliable analytical data products. Implement and optimize data transformations using tools such as dbt, ensuring high data quality, performance, and maintainability. Build and evolve semantic layers and KPI frameworks that support dashboards, reporting, experimentation, and advanced analytics use cases. Define best practices for data modeling, metric definitions, and analytics engineering standards across teams. Collaborate with data engineering, analytics, and business teams to support end-to-end delivery of data-driven solutions. Requirements: 5+ years of experience in data analytics engineering, data modeling, BI engineering, or similar roles. Strong expertise in SQL, with experience in data modeling, query optimization, and working with large-scale datasets. 3+ years of experience with Python and dbt for data transformation and analytics engineering workflows. Deep understanding of modern data warehousing concepts and cloud data platforms such as Snowflake or similar. Experience building metrics layers, semantic models, and self-service analytics environments. Strong analytical and problem-solving skills with the ability to manage multiple complex data projects. Excellent communication and documentation skills, with the ability to explain technical concepts to non-technical audiences. Experience working in remote, cross-functional environments with strong ownership and autonomy. Familiarity with cloud platforms (AWS, GCP, or Azure) and source control tools such as Git is a plus. Experience with semi-structured data formats (e.g., JSON) and BI tools is
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