Sr. Analytics Engineer

Sr. Analytics Engineer

The Sr. Analytics Engineer will play a key role in building and scaling a modern analytics foundation that enables trusted, data-driven decision-making across the organization. This position focuses on transforming complex data into reliable, well-documented models that support analytics, reporting, and advanced data initiatives. You will take ownership of analytics engineering practices, from data modeling and quality assurance to semantic layers and BI enablement. Working with cross-functional teams, you will help establish scalable solutions that improve data accessibility, consistency, and business impact. The role requires deep technical expertise in SQL, Python, dbt, and cloud data platforms, combined with strong ownership and collaboration skills. You will contribute to an innovative, AI-enabled environment where data quality, automation, and operational excellence are core priorities.

Accountabilities:

• Design, build, and maintain scalable, modular, and well-tested data transformation models using dbt, following modern data modeling principles such as Medallion architecture and dimensional modeling.
• Transform raw data into trusted analytics-ready datasets and data marts that support business reporting, executive dashboards, and self-service analytics.
• Develop reusable modeling frameworks, macros, packages, and standards to improve consistency, maintainability, and performance across the data warehouse.
• Optimize cloud data warehouse performance and cost through effective use of clustering, materializations, incremental models, and other optimization techniques.
• Own the semantic and metrics layer by defining governed, version-controlled business metrics that ensure consistency across reporting and analytics platforms.
• Partner with BI teams and analysts to deliver reliable datasets through tools such as Tableau, Power BI, and Looker.
• Create and maintain comprehensive documentation, data dictionaries, lineage, and analytics model libraries across key business domains.
• Build automated data quality checks, validation frameworks, anomaly detection processes, and monitoring solutions to maintain data accuracy and reliability.
• Support machine learning and AI initiatives by preparing clean, feature-ready datasets and collaborating on analytics solutions using modern AI and data platforms.
• Mentor junior team members and promote best practices in SQL optimization, dbt development, analytics engineering workflows, and data documentation.
• Collaborate with product, engineering, analytics, and business stakeholders to translate requirements into scalable technical solutions.
• Participate in production support and on-call rotations to monitor data pipeline health and resolve data-related issues when required.

Requirements:

• 6–8+ years of experience in analytics engineering, data analytics, or data engineering, with strong expertise in data modeling and transformation.
• Proven experience owning the full development lifecycle, including requirements gathering, solution design, testing, deployment, and production support.
• Expert-level proficiency in SQL and Python for data transformation, automation, and analytics engineering workflows.
• Extensive hands-on experience with dbt Core or dbt Cloud, including building modular, tested, and version-controlled transformation pipelines.
• Strong experience with modern cloud data platforms such as Snowflake or Databricks, ideally within an AWS-based environment.
• Experience designing governed metrics, semantic layers, and curated datasets for business intelligence platforms including Tableau, Power BI, or Looker.
• Knowledge of analytics modeling across business functions such as Finance, Sales, and Operations is highly desirable.
• Experience with data governance, profiling, cataloging, and quality management tools is a plus.
• Strong understanding of automated testing frameworks, data validation practices, and analytics pipeline monitoring.
• Excellent documentation skills, with experience creating technical specifications, playbooks, and data dictionaries.
• Strong ownership mindset, problem-solving abilities, and the ability to collaborate effectively with technical and business teams.
• Relevant certifications such as dbt Analytics Engineering Certification, SnowPro Core, Databricks Certified Data Engineer Professional, or AWS Certified Data Engineer are considered a plus.

Benefits:

• Competitive compensation package aligned with experience and market standards.
• Fully remote work environment with flexibility and autonomy.
• Opportunity to contribute to a growing, AI-enabled technology environment.
• High-impact role supporting analytics, machine learning, enterprise reporting, and business innovation.
• Collaboration with multidisciplinary teams across data, product, engineering, analytics, and business functions.
• Culture focused on ownership, accountability, continuous improvement, and measurable impact.
• Flexible working schedule designed to support collaboration with international teams.
• Opportunities for professional growth and technical development.