Senior Analytics Engineer - Marketing Data

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Analytics Engineer – Marketing Data based in Saudi Arabia. Join a data-driven environment where analytics engineering plays a key role in transforming marketing data into actionable business insights. In this role, you will design and optimize data models that support marketing analytics and business decision-making. You will work extensively with Google Analytics data, Snowflake, SQL, and Python to build reliable, scalable data solutions. Partnering with business stakeholders, you will translate complex requirements into clear technical deliverables. You will help ensure data quality, consistency, and accuracy throughout the entire data pipeline. Working within a collaborative, version-controlled development environment, you will contribute to robust engineering practices and continuous improvement. This opportunity is ideal for an experienced analytics engineer who combines strong technical expertise with business understanding and a proactive, problem-solving mindset. Accountabilities • Design, develop, and maintain scalable data models, transformations, and materializations in Snowflake to support marketing analytics and business reporting. • Work hands-on with Google Analytics data in a production environment, ensuring that marketing data is accurately structured and readily available for analysis. • Develop, review, troubleshoot, and optimize SQL queries and Python code to improve data processing, reliability, and performance. • Analyze API documentation and translate endpoints, objects, fields, and identifiers into practical data integration solutions aligned with business requirements. • Implement and maintain data validation practices across multiple stages of data pipelines to ensure data integrity, consistency, and accuracy. • Apply Git-based development workflows, including version control, code reviews, testing, and CI/CD practices, to maintain high engineering standards. • Collaborate with stakeholders to gather requirements, clarify ambiguous business needs, structure discussions, and translate findings into precise, actionable technical specifications. • Identify opportunities to improve data models, transformation logic, pipeline reliability, and overall analytics engineering processes. Requirements • At least 5 years of relevant professional experience in analytics engineering, data engineering, marketing data, or a closely related field. • Hands-on professional experience working with Google Analytics data in a business or production environment. Academic coursework, certifications, or self-directed projects alone do not meet this requirement. • Professional experience supporting data initiatives within a retail or e-commerce environment. • Advanced SQL skills, including experience designing fit-for-purpose data models, transformations, and materializations in Snowflake. • Working proficiency in Python, with the ability to read, evaluate, debug, troubleshoot, and improve 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 development workflows, including code reviews, testing, and CI/CD. • Strong understanding of data quality and validation techniques across different stages of a data pipeline. • Excellent stakeholder management and requirements-gathering skills, with the ability to bring structure to ambiguous discussions and turn business needs into clear technical solutions. • Exceptional written and verbal communication skills in English, with the ability to communicate technical concepts clearly to both technical and non-technical audiences. • A proactive, analytical, and detail-oriented mindset, combined with strong problem-solving skills and the ability to work effectively in a remote environment. Benefits • Remote work: Work remotely from Anywhere, offering flexibility in your working environment. • Long-term engagement: An opportunity to contribute to ongoing data engineering and marketing analytics initiatives through a long-term contract. • Technical impact: Work with modern data technologies, including Snowflake, SQL, Python, Google Analytics, and API integrations. • Business exposure: Collaborate with stakeholders to solve meaningful business challenges and support data-informed decision-making. • Professional development: Strengthen your expertise in analytics engineering, data modeling, marketing data, and scalable data pipeline development. How Jobgether Works We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. 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At least 5 years of relevant professional experience in analytics engineering, data engineering, marketing data, or a closely related field. Hands-on professional experience working with Google Analytics data in a business or production environment. Professional experience supporting data initiatives within a retail or e-commerce environment. Advanced SQL skills, including experience designing fit-for-purpose data models, transformations, and materializations in Snowflake. Working proficiency in Python, with the ability to read, evaluate, debug, troubleshoot, and improve 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 development workflows, including code reviews, testing, and CI/CD. Strong understanding of data quality and validation techniques across different stages of a data pipeline. Excellent stakeholder management and requirements-gathering skills, with the ability to bring structure to ambiguous discussions and turn business needs into clear technical solutions. Exceptional written and verbal communication skills in English, with the ability to communicate technical concepts clearly to both technical and non-technical audiences. A proactive, analytical, and detail-oriented mindset, combined with strong problem-solving skills and the ability to work effectively in a remote environment.

Design, develop, and maintain scalable data models, transformations, and materializations in Snowflake to support marketing analytics and business reporting. Work hands-on with Google Analytics data in a production environment, ensuring that marketing data is accurately structured and readily available for analysis. Develop, review, troubleshoot, and optimize SQL queries and Python code to improve data processing, reliability, and performance. Analyze API documentation and translate endpoints, objects, fields, and identifiers into practical data integration solutions aligned with business requirements. Implement and maintain data validation practices across multiple stages of data pipelines to ensure data integrity, consistency, and accuracy. Apply Git-based development workflows, including version control, code reviews, testing, and CI/CD practices, to maintain high engineering standards. Collaborate with stakeholders to gather requirements, clarify ambiguous business needs, structure discussions, and translate findings into precise, actionable technical specifications. Identify opportunities to improve data models, transformation logic, pipeline reliability, and overall analytics engineering processes.