Company
Media
AnalyticsEngineer-DigitalMedia
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Analytics Engineer - Digital Media. Skills: Data modeling, Data pipelines, SQL transformation. Design scalable data models. Build scalable data models”
Industry & Context.
Problem-solving
What They're Looking For.
Must Have
2–4+ years of experience, SQL expertise, Hands-on experience with cloud data warehouses, Proven experience working with business stakeholders, Experience building and maintaining data documentation, Experience using Git and version control
Nice to Have
Familiarity with dbt or similar, Python experience for data pipelines or automation is a plus, AWS QuickSight experience, Omni experience
What You'll Do.
Design scalable data models
Build scalable data models
Maintain scalable data models
Enhance cloud-based analytics warehouse architecture
Optimize cloud-based analytics warehouse architecture
Define standards for schema design
Enforce standards for schema design
Define standards for naming conventions
Enforce standards for naming conventions
Define standards for data lineage documentation
Enforce standards for data lineage documentation
Develop data pipelines
Maintain data pipelines
Translate business needs into data solutions
Create data documentation
Maintain data documentation
Deliver analytics-ready datasets
Contribute to dashboard development
Implement data quality processes
Monitor data quality processes
Identify improvements in data accessibility
Drive improvements in data accessibility
Identify improvements in data consistency
Drive improvements in data consistency
Identify improvements in data usability
Drive improvements in data usability
Promote analytics engineering best practices
Promote version control
Promote reproducible workflows
How You'll Work.
Team & Collaboration
Collaborate with data engineering teams; Partner with stakeholders
Communication Scope
Communication skills
Process & Methodology
Version control, Code reviews, Reproducible workflows
Full Job Description
## Accountabilities Design, build, and maintain scalable data models in Amazon Redshift to support reporting, dashboards, and analytical use cases Collaborate with data engineering teams to enhance and optimize cloud-based analytics warehouse architecture Define and enforce standards for schema design, naming conventions, and data lineage documentation Develop and maintain reliable data pipelines that ensure clean, structured, and accessible data for business users Partner with stakeholders to gather requirements and translate business needs into scalable data solutions Create and maintain comprehensive data documentation, including metric definitions and transformation logic Support BI platforms by delivering analytics-ready datasets and contributing to dashboard development Implement and monitor data quality processes, including testing, validation, and alerting mechanisms Identify and drive improvements in data accessibility, consistency, and usability across the organization Promote analytics engineering best practices, including version control, code reviews, and reproducible workflows Requirements: 2–4+ years of experience in Analytics Engineering, Data Engineering, Business Intelligence, Data Analytics, or Data Science Strong SQL expertise with the ability to design efficient and scalable queries and data models Hands-on experience with cloud data warehouses, ideally AWS and Amazon Redshift Proven experience working with business stakeholders to translate requirements into data solutions Familiarity with dbt or similar SQL transformation and version-controlled workflows is highly desirable Strong understanding of data modeling concepts (dimensional modeling, star schema, snowflake schema, SCDs) Experience building and maintaining data documentation, metrics definitions, and data catalogs Familiarity with BI tools such as Tableau, AWS QuickSight, Omni, or similar platforms Experience using Git and version control for managing SQL and data pipelines Strong anal
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