Senior Data Operations Engineer

Senior Data Operations Engineer

This role sits at the core of a modern data ecosystem, supporting large-scale data operations across cloud-based warehouses and analytics platforms. You will work on designing, optimizing, and maintaining end-to-end data pipelines that power critical business decisions across multiple teams. The position blends hands-on engineering with collaboration, enabling you to influence architecture, improve performance, and ensure high data reliability. You will partner with engineers, analysts, data scientists, and business stakeholders to translate complex data requirements into scalable solutions. This is a highly visible role where your work directly impacts operational efficiency, cost optimization, and data-driven decision-making. The environment is fast-paced, collaborative, and built around modern data stack technologies such as Snowflake, Databricks, Azure, and DBT. It is well-suited for someone who enjoys solving complex data challenges while mentoring others and driving best practices.

Accountabilities:

• Manage and optimize end-to-end data pipelines, from ingestion and transformation to deployment of data models, ensuring reliability, scalability, and performance.
• Collaborate with business and technical stakeholders to understand data requirements, system configurations, and downstream analytics needs.
• Monitor, troubleshoot, and resolve data pipeline issues, identifying bottlenecks and implementing performance improvements and cost optimizations.
• Develop, review, and test code using SQL and Python, ensuring strong data quality, consistency, and adherence to engineering standards.
• Support architectural discussions and contribute to the design of scalable data solutions within cloud environments such as Azure and Snowflake.
• Drive automation of manual processes and promote best practices in data engineering, governance, and operational efficiency.
• Provide technical guidance, training, and support to internal users, fostering a strong data-driven culture across teams.

Requirements

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience with strong relevant background in data engineering roles.
• 5+ years of experience working in data operations, data engineering, or analytics environments involving large-scale data systems.
• Strong proficiency in SQL and working knowledge of Python for data processing, automation, and optimization tasks.
• Hands-on experience with cloud data platforms such as Snowflake and cloud environments like Azure, AWS, or GCP.
• Experience with ELT/ETL orchestration tools such as Azure Data Factory, Airflow, or similar workflow management systems.
• Strong understanding of data modeling, data warehousing, and data architecture principles.
• Experience with Git-based version control and collaborative engineering workflows.
• Excellent analytical, communication, and problem-solving skills with the ability to explain complex technical concepts to non-technical stakeholders.
• Strong team player with experience working in agile development environments.
• Exposure to performance tuning, cost optimization, and large-scale data warehouse migration projects is a plus.

Benefits

• Fully remote work opportunity from India with structured working hours aligned to EST.
• Long-term contract engagement with potential for extension based on performance and business needs.
• Exposure to modern data stack technologies including Snowflake, Databricks, Azure, DBT, and Airflow.
• Opportunity to work on large-scale, high-impact data systems supporting enterprise-level decision-making.
• Strong focus on learning, mentorship, and collaboration with experienced data engineering teams.
• Hands-on experience with cloud optimization, automation, and data pipeline modernization initiatives.
• Inclusive, collaborative environment that encourages innovation and continuous improvement.