Senior Data Operations Engineer
Fully remote work opportunity from India, ESTContractSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLETLSnowflakeAirflowAzureData engineeringData modelingdbtDatabricks
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.
Responsibilities
- 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.
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