 
						Data Engineer
3 weeks ago
Job Description:
Must Have:
- Must have 6+Years of experience
- Programming Language: Python
- AWS Services: IAM, Networking, Lake Formation, EMR, MWAA, Athena, Glue ETL, Glue Catalog, S3, Lambda, API Gateway, Event Bridge, SQS, SNS, Kinesis, DMS
- Automation: Terraform (IaC), GitHub Actions
- UI Tech (for a few resources): Angular
- Architecture: Data Mesh, Micro Services
Strong Experience in PySpark:
- Hands-on expertise in building scalable data pipelines using PySpark.
- Proficiency in using Spark SQL, DataFrame, and RDD APIs to implement complex business logic.
Proficient Programming Skills:
- Solid coding skills in Python (preferred), with strong fundamentals in data structures, algorithms, and software engineering principles.
Data Pipeline Development:
- Proven experience designing, developing, and maintaining batch and streaming data pipelines.
- Understanding of ETL/ELT processes and best practices for data transformation, data quality, and performance optimization.
Knowledge of Modern Data Engineering Ecosystem:
- Familiarity with the current data engineering landscape including distributed data processing, storage systems, and workflow orchestration tools.
- Tools/technologies could include Apache Airflow, dbt, Delta Lake, etc.
Cloud Data Platform Experience (Preferably AWS)
- Experience working with cloud services such as:
- AWS S3 (data lake)
- AWS Glue / EMR (for Spark workloads)
- AWS Lambda, Step Functions, or similar for orchestration/integration
- Redshift or other cloud data warehouses
Spark API Expertise for Business Logic Implementation
- Ability to choose and apply the right Spark APIs (Data Frame, Dataset, RDD) for performance-efficient implementation of business logic at scale.
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