Senior Data Engineer; r

Position: Senior Data Engineer (Immediate Joiner) EazyML, recognized by Gartner, EazyML () specializes in Responsible AI. Our solutions facilitate proactive compliance and sustainable automation and the company is associated with breakthrough startups like Amelia.ai. This is a full-time Remote role for a Senior Data Engineer with experience in Databricks We're hiring a Senior Data Engineer to design, build, and optimize scalable data platforms leveraging Databricks. This role will be responsible for delivering reliable, high-performance data pipelines and analytics-ready datasets, while providing technical leadership and mentoring within the data engineering team. Required Qualifications 6+ years of experience in Data Engineering, ETL Development, Database Administration. Strong hands-on experience with Databricks in production environments Advanced SQL skills and solid expertise in data modeling Proficiency in Python, SQL, Py Spark Strong experience with Apache Spark and Py Spark Experience working with Delta Lake, schema evolution, and data versioning Experience with one or more cloud platforms (Azure, AWS or GCP ) Experience building scalable, reliable, fault-tolerant data pipelines Solid understanding of distributed data systems Exposure to ML pipelines or feature stores (Databricks Feature Store preferred) Key Skills Databricks & Apache Spark Lakehouse and Data Warehouse architecture Advanced SQL and performance tuning Cloud-native data engineering Scalability, reliability, and cost optimization Technical leadership and mentoring Design and implement scalable data pipelines using Databricks (PySpark, Delta Lake) Develop and optimize ELT pipelines loading data for analytics and reporting Architect and maintain lakehouse and warehouse solutions following Bronze, Silver, and Gold data layer patterns Build batch and streaming pipelines using Databricks Jobs and Spark Structured Streaming Design data models optimized for Snowflake (star/snowflake schemas, dimensional modeling) Optimize Spark jobs and Snowflake queries for performance and cost efficiency Implement data quality checks, monitoring, and data validation across Databricks and Snowflake Integrate Databricks and Snowflake with orchestration tools (Azure Data Factory, etc.) Ensure data security, governance, role-based access control, and compliance standards Collaborate with Data Analysts and Data Scientists to deliver analytics and ML-ready datasets Troubleshoot complex pipeline failures and perform root-cause analysis Mentor junior engineers, conduct code reviews, and enforce engineering best practices Contribute to data architecture decisions, tooling evaluation, and roadmap planning Maintain clear documentation of pipelines, data models, and system architecture Experience: 6+ years | CS/IT degree preferred Position: Senior Data Engineer (Immediate Joiner) EazyML, recognized by Gartner, EazyML () specializes in Responsible AI. Our solutions facilitate proactive compliance and sustainable automation and the company is associated with breakthrough startups like Amelia.ai. This is a full-time Remote role for a Senior Data Engineer with experience in Databricks We're hiring a Senior Data Engineer to design, build, and optimize scalable data platforms leveraging Databricks. This role will be responsible for delivering reliable, high-performance data pipelines and analytics-ready datasets, while providing technical leadership and mentoring within the data engineering team. Required Qualifications 6+ years of experience in Data Engineering, ETL Development, Database Administration. Strong hands-on experience with Databricks in production environments Advanced SQL skills and solid expertise in data modeling Proficiency in Python, SQL, Py Spark Strong experience with Apache Spark and Py Spark Experience working with Delta Lake, schema evolution, and data versioning Experience with one or more cloud platforms (Azure, AWS or GCP ) Experience building scalable, reliable, fault-tolerant data pipelines Solid understanding of distributed data systems Exposure to ML pipelines or feature stores (Databricks Feature Store preferred) Key Skills Databricks & Apache Spark Lakehouse and Data Warehouse architecture Advanced SQL and performance tuning Cloud-native data engineering Scalability, reliability, and cost optimization Technical leadership and mentoring Design and implement scalable data pipelines using Databricks (PySpark, Delta Lake) Develop and optimize ELT pipelines loading data for analytics and reporting Architect and maintain lakehouse and warehouse solutions following Bronze, Silver, and Gold data layer patterns Build batch and streaming pipelines using Databricks Jobs and Spark Structured Streaming Design data models optimized for Snowflake (star/snowflake schemas, dimensional modeling) Optimize Spark jobs and Snowflake queries for performance and cost efficiency Implement data quality checks, monitoring, and data validation across Databricks and Snow