Senior Data Engineer
New
W
WeekdaySoftware Development
IndiaFull-TimeSenior
Salary1,100,000 - 5,000,000 INR per year
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Job Details
- Experience
- 6+ yrs
- Required Skills
- PythonSQLETLData engineeringSparkData modelingDatabricks
Requirements
- 5+ years of hands-on experience as a Data Engineer with expertise in enterprise-scale data engineering solutions.
- Strong experience with Microsoft Azure Fabric, Databricks, and Azure data services in production environments.
- Solid understanding of SQL, data modeling, data warehousing concepts, and cloud-based data platforms.
- Proven experience designing and implementing scalable ETL/ELT pipelines and enterprise data integration solutions.
- Proficiency in Python, Apache Spark, or similar technologies for large-scale data processing.
- Experience optimizing cloud-based data workflows for performance, scalability, reliability, and cost efficiency.
- Strong analytical and problem-solving skills with the ability to troubleshoot complex production issues effectively.
- Comfortable using AI-assisted development tools and automation techniques to improve engineering productivity and solution quality.
- Excellent communication and stakeholder management skills, with the ability to collaborate effectively across global, cross-functional teams.
- A proactive, ownership-driven mindset with the flexibility to adapt to evolving priorities while consistently delivering high-quality, business-critical data solutions.
Responsibilities
- Design, develop, and maintain scalable data pipelines using Microsoft Azure Fabric, Databricks, and Azure data services.
- Build and optimize robust ETL/ELT processes for structured, semi-structured, and unstructured data across enterprise environments.
- Collaborate with business stakeholders to understand data requirements and translate them into scalable technical solutions.
- Develop and maintain reliable data integration workflows that ensure data accuracy, consistency, and availability.
- Optimize data processing pipelines for performance, scalability, cost efficiency, and operational reliability.
- Leverage AI-assisted development tools and modern engineering practices to accelerate solution delivery and improve code quality.
- Monitor, troubleshoot, and resolve production data pipeline issues while proactively identifying opportunities for automation and optimization.
- Work closely with architects, analysts, developers, and cross-functional teams throughout the project lifecycle to deliver high-quality data solutions.
- Implement best practices for data engineering, governance, documentation, testing, and operational support.
- Take ownership of project deliverables by ensuring quality, meeting timelines, communicating risks proactively, and continuously improving engineering processes.
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