Senior Data Engineer
New
J
JobgetherData engineering
Fully remote opportunity available to candidates based anywhere in India, Provide overlap with U.S. East Coast working hours when critical discussions require it, potentially until 1:00 PM ET.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of hands-on professional experience in Data Engineering or a closely related role.
- Required Skills
- PythonSQLData engineeringSparkData modelingDatabricks
Requirements
- Have 5+ years of hands-on professional experience in Data Engineering or a closely related role.
- Have strong production experience with Microsoft Azure Fabric, Databricks, and Azure data services.
- Have solid knowledge of SQL, data modeling, data integration, and modern data engineering principles.
- Have experience designing and building enterprise-grade data pipelines and ETL/ELT solutions.
- Have experience working with cloud-based data platforms in production environments.
- Have experience working with structured and unstructured data.
- Have a strong understanding of scalable data architecture, pipeline performance, reliability, and optimization.
- Be proficient in Python, Apache Spark, or comparable data processing technologies; this is preferred.
- Be able to troubleshoot complex data and production issues and implement sustainable solutions.
- Be comfortable using AI tools and automation to improve engineering productivity and solution quality.
- Be able to manage work independently, adapt to changing priorities, and collaborate with cross-functional and global teams.
- Be able to communicate technical concepts clearly to technical and business stakeholders and coordinate across time zones.
Responsibilities
- Design, develop, and maintain scalable data pipelines using Microsoft Azure Fabric, Databricks, and Azure data services.
- Build reliable ETL and ELT processes for structured and unstructured data.
- Develop secure, scalable, maintainable, production-ready data integration solutions.
- Translate business stakeholders’ data requirements into technical solutions.
- Apply data modeling and SQL practices to create efficient, reliable data structures.
- Optimize data processing workflows for performance, scalability, reliability, and cost efficiency.
- Use Python, Spark, and related technologies to develop and enhance data engineering solutions.
- Monitor and troubleshoot production data issues and identify opportunities for improvement.
- Collaborate with architects, analysts, developers, and other technical stakeholders throughout the project lifecycle.
- Own assigned work end to end, manage delivery timelines, and keep stakeholders informed of risks and progress.
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