Senior Analytics Engineer

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Requirements:

• Experience: 5+ years of experience in data engineering or analytics engineering roles, with a proven track record of leading complex data projects and initiatives.
• Technical Expertise: Deep expertise in SQL, DBT, and data modeling, with a strong understanding of data pipeline design, ETL processes, and data warehousing.
• Software Engineering Skills: Proficiency in software engineering principles, including CI/CD pipelines, version control (e.g., Git), and scripting languages (e.g., Python).
• Data Tools Proficiency: Hands-on experience with tools like Snowflake, DBT, and Looker. Familiarity with additional tools and platforms (e.g., AWS, Kubernetes) is a plus.
• Problem-Solving: Strong analytical and problem-solving skills, with the ability to diagnose and resolve complex technical issues related to data infrastructure.
• Leadership: Demonstrated ability to mentor and lead junior engineers, with a focus on fostering a collaborative and high-performance team environment.
• Communication: Excellent communication skills, with the ability to clearly and concisely convey complex technical concepts to both technical and non-technical stakeholders.
• Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.

Responsibilities:

• Data Pipeline Leadership: Design, develop, and maintain highly scalable and efficient data pipelines, ensuring timely and accurate collection, transformation, and integration of data from various sources.
• Advanced Data Modeling: Architect and implement robust data models and data warehousing solutions that enable efficient storage, retrieval, and analysis of large, complex datasets.
• Cross-Functional Collaboration: Work closely with data scientists, analysts, and business stakeholders to understand data requirements, translating them into actionable data models and insights.
• Data Quality Assurance: Implement and oversee rigorous data validation, cleansing, and error-handling mechanisms to maintain high data quality and reliability.
• Performance Optimization: Continuously monitor and optimize data pipeline performance, identifying and resolving bottlenecks and inefficiencies to maintain optimal system responsiveness.
• Mentorship and Leadership: Provide guidance and mentorship to junior analytics engineers, fostering a collaborative and learning-oriented environment.
• Strategic Contribution: Contribute to the strategic direction of data initiatives, staying abreast of industry best practices, emerging technologies, and trends in data engineering and analytics.
• Documentation & Knowledge Sharing: Build and maintain user-facing documentation for key processes, metrics, and data models to enhance the data-driven culture within the organization.