AWS Data Engineer
2 weeks ago
We are looking for an experienced AWS Data Engineer with 6+ years of experience, including at least 2+ years of hands-on work with Databricks. The ideal candidate will be responsible for designing, building, and optimizing scalable data pipelines using AWS services and Databricks. You will play a key role in processing large datasets, enabling analytics, and ensuring data security, performance, and reliability.
Key Responsibilities:
Data Pipeline Development:
- Design, develop, and manage scalable ETL/ELT pipelines using AWS services and Databricks.
- Ensure efficient data ingestion, processing, and transformation for analytical and reporting needs.
Data Integration & Transformation:
- Process structured, semi-structured, and unstructured data into AWS Data Lake or Databricks.
- Develop advanced data transformation workflows using PySpark, Databricks SQL, or Scala.
Databricks & AWS Architecture:
- Configure and optimize Databricks clusters, notebooks, and jobs for performance and cost efficiency.
- Design and implement solutions using AWS-native services like S3, Glue, Redshift, EMR, Lambda, Kinesis, and Athena.
Performance Tuning & Optimization:
- Optimize data pipelines, queries, and storage to ensure scalability, cost-effectiveness, and high availability.
- Implement best practices for monitoring, debugging, and troubleshooting issues in cloud-based data pipelines.
Collaboration & Documentation:
- Work closely with Data Analysts, Data Scientists, and Engineers to understand business needs and deliver data-driven solutions.
- Maintain comprehensive documentation for data pipelines, workflows, and architectures.
Security & Monitoring:
- Ensure data security, compliance, and governance across AWS environments.
- Implement monitoring solutions using CloudWatch, Datadog, or equivalent tools to track pipeline health and performance.
Required Skills & Qualifications:
6+ years of experience in Data Engineering, with at least 2+ years of hands-on experience in Databricks.
Strong expertise in AWS services like S3, Glue, Redshift, DynamoDB, Lambda, EMR, and Athena.
Proficiency in Python (PySpark), Scala, or SQL for data processing and transformation.
Extensive experience with Databricks Workspace, Delta Lake, and managing Databricks jobs and pipelines.
Strong knowledge of Apache Spark and Big Data processing frameworks.
Experience with modern data warehousing solutions like Redshift, Snowflake, or Databricks SQL.
Familiarity with Git, Terraform, and CI/CD pipelines for deploying data solutions.
Strong debugging, performance tuning, and monitoring experience using tools like CloudWatch and Datadog.
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