TELECOMMUTE Principal Data Architect

This is a strategic and hands-on leadership role focused on shaping the future of a modern, cloud-native data and AI platform. You will define the architecture that powers large-scale data processing, advanced analytics, and machine learning capabilities. Working in a fast-growing, globally distributed environment, you will collaborate with cross-functional teams to solve complex data challenges and drive innovation. This role offers the opportunity to influence technical direction, design scalable systems, and enable data-driven decision-making at scale. It is ideal for someone who thrives at the intersection of architecture, engineering, and data science, and is passionate about building high-impact solutions. Accountabilities:

• Define and drive the technical vision for data architecture, ensuring scalability, performance, and long-term sustainability.
• Design and implement large-scale data and machine learning systems capable of processing high volumes of real-time data.
• Lead architectural decision-making processes, ensuring solutions are robust, future-proof, and aligned with business goals.
• Build and optimize data pipelines, feature engineering workflows, and model deployment frameworks.
• Collaborate with DevOps and engineering teams to ensure infrastructure supports efficient training and inference workloads.
• Develop and evolve data models supporting analytics, predictive capabilities, and business growth.
• Mentor and guide data scientists and engineers, promoting best practices in MLOps, system design, and statistical rigor.

• Degree in Computer Science, Engineering, Applied Mathematics, or a related STEM field, or equivalent practical experience.
• 5+ years of experience across data engineering, data architecture, and data science roles.
• Proven experience designing and deploying distributed data systems at scale.
• Strong expertise in cloud platforms such as AWS, GCP, or Azure, and modern data tools like Snowflake, Databricks, or BigQuery.
• Solid understanding of data modeling techniques, including relational, dimensional, and NoSQL approaches.
• Hands-on experience with data pipeline and orchestration tools such as Airflow, dbt, Spark, or Kafka.
• Advanced proficiency in Python and SQL, with experience using data science libraries (e.g., pandas, NumPy, scikit-learn).
• Experience building, deploying, and maintaining machine learning models in production environments.
• Familiarity with MLOps practices, data governance, security, and compliance standards.
• Bonus: Experience with AI-driven systems, RAG pipelines, or usage-based billing models.

• Competitive salary aligned with experience and expertise
• Fully remote work environment with flexible working arrangements
• Opportunities for career advancement and continuous professional development
• Exposure to cutting-edge technologies in data, AI, and cloud ecosystems
• Collaborative and inclusive work culture focused on innovation and growth
• Work-life balance support and flexible scheduling