Senior Cloud Data Engineer

5 days ago


Noida India Umanist NA Full time

Job Description Senior Cloud Data Engineer 241 Key Responsibilities - Develop and maintain end-to-end data pipelines for ingesting, wrangling, transforming, and joining data from diverse sources. - Design and implement scalable, high-performance ETL pipelines using tools such as AWS Glue, Databricks, Apache Airflow, and DataProc. - Develop data models, schemas, and storage solutions for relational, NoSQL, and lakehouse environments (Snowflake, BigQuery, Delta Lake). - Optimize data pipelines for performance and cost, including infrastructure considerations. - Implement CI/CD pipelines for data solutions and infrastructure using tools like GitHub Actions, Jenkins, Terraform, or similar. - Collaborate with teams to define architecture, design patterns, and reusable solutions for complex distributed systems. - Perform code reviews, debugging, testing, and validation to ensure high-quality and reliable data solutions. - Monitor pipeline performance, identify defects, perform root cause analysis (RCA), and implement preventive measures. - Work with AI/ML teams to enable LLM-based data classification, data enrichment, and data governance automation. - Support documentation, including design documents, architecture diagrams, source-target mappings, test cases, and release notes. - Mentor and guide team members on best practices, coding standards, and technical certifications. - Interface with stakeholders to clarify requirements, present design options, and provide guidance on implementation. Required Skills & Expertise - Strong proficiency in Python, PySpark, and SQL for large-scale data processing. - Hands-on experience with cloud platforms (AWS, Azure, or GCP) and their data services (S3, Redshift, RDS, BigQuery, Lambda, Glue, DataProc, CloudFormation, CloudWatch). - Extensive experience in ETL pipeline development, data lakes, data warehouses, and distributed systems. - Knowledge of IaC tools such as Terraform for provisioning and managing cloud infrastructure. - Experience with CI/CD pipelines and DevOps practices for data and infrastructure deployment. - Familiarity with data governance, compliance, security, and performance optimization. - Experience with NoSQL databases (Document DB, MongoDB, DynamoDB) and analytical data formats (Parquet, Iceberg). - Working knowledge of LLM integration for data classification, RAG workflows, or AI-assisted data processing is a plus. Skills: classification,design,infrastructure,aws,cloud,ci,pipelines,cd,data,architecture



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