Senior Analytics Engineer - Marketing Data
New
J
JobgetherMarketing analytics
Based in IndiaContractSenior
Salary not disclosed
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Job Details
- Languages
- Exceptional written and verbal communication skills in English
- Experience
- At least 5 years of relevant professional experience
- Required Skills
- PythonSQLGitSnowflakeGoogle AnalyticsCI/CD
Requirements
- At least 5 years of relevant professional experience in analytics engineering, data engineering, marketing data, or a closely related field.
- Professional experience working with Google Analytics data in a business or production environment.
- Professional experience supporting data initiatives in retail or e-commerce.
- Advanced SQL skills, including designing data models, transformations, and materializations in Snowflake.
- Working proficiency in Python, including reading, evaluating, debugging, troubleshooting, and improving code written by others.
- Experience interpreting API documentation and mapping API endpoints, objects, fields, and identifiers to business and technical requirements.
- Practical experience with Git and version-controlled workflows, including code reviews, testing, and CI/CD.
- Strong understanding of data quality and validation techniques across data pipeline stages.
- Stakeholder management and requirements-gathering skills, including structuring ambiguous discussions and translating business needs into technical solutions.
- Excellent written and verbal communication skills in English, including explaining technical concepts to technical and non-technical audiences.
- Proactive, analytical, detail-oriented, and problem-solving approach.
Responsibilities
- Design, develop, and maintain scalable Snowflake data models, transformations, and materializations for marketing analytics and business reporting.
- Work with Google Analytics data in a production environment and ensure it is accurately structured for analysis.
- Develop, review, troubleshoot, and optimize SQL queries and Python code.
- Interpret API documentation and translate endpoints, objects, fields, and identifiers into data integration solutions.
- Implement and maintain data validation across pipeline stages.
- Apply Git-based workflows, including version control, code reviews, testing, and CI/CD.
- Gather stakeholder requirements and translate business needs into actionable technical specifications.
- Identify improvements to data models, transformation logic, pipeline reliability, and analytics engineering processes.
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