Technical Architect - Data Engineering

Lead and manage a high‑performing team of data engineers to design and build scalable, secure, and cost‑effective cloud‑based data warehouse solutions. Drive end‑to‑end ETL/ELT pipelines using PySpark, Python, and SQL, champion cloud‑native development, and enforce engineering best practices.

• Lead and manage a high-performing team of data engineers across multiple projects and geographies.
• Define and drive the architecture of scalable, secure, and cost-effective cloud-based data warehouse solutions.
• Oversee the development of robust ETL/ELT pipelines using PySpark, Python, and SQL.
• Champion the adoption, integration, and enhancement of in-house data tools, ensuring alignment with business and technical goals.
• Collaborate with business, analytics, and product teams to translate data requirements into actionable solutions.
• Write efficient, reusable, and well-documented code.
• Maintain and tune existing Spark applications to the fullest.
• Find opportunities for optimizing existing Spark applications.
• Work closely with QA, Operations and various teams to deliver error-free software on time.
• Actively lead/participate daily agile/scrum meetings.
• Establish and enforce engineering best practices, including code quality, testing, documentation, and CI/CD.
• Drive data governance, quality, lineage, and compliance across platforms.
• Manage vendor relationships, and strategic partnerships related to data infrastructure.
• Mentor senior engineers and technical leads, fostering a culture of innovation and continuous improvement.

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• 10-16 years of experience in data engineering, with a strong focus on cloud-native data solutions.
• Deep expertise in Python, PySpark, and SQL for data processing and analytics.
• Proven experience with Cloud platforms (AWS, Azure, or GCP) and their data services.
• Strong understanding of data warehousing concepts, data modeling, and distributed systems.
• Experience working with and enhancing internal/in-house tools for data processing and analytics.
• Excellent leadership, communication, and stakeholder management skills.
• Experience with orchestration tools like Apache Airflow.
• Knowledge of BI tools (e.g., Power BI, Tableau) and data visualization best practices.
• Experience in Agile environments and DevOps practices.