Das ist der Job
Build ETL/ELT processes to ingest, transform, and load data from various sources into data warehouses and data lakes.
Darum lohnt es sich
Design data warehouse solutions using Redshift and integrate with existing RDS/Aurora databases managed by the DBA team. Work with the DBA team to optimize data access patterns and query performance across relational and analytical databases.
Responsibilities Design and implement scalable data pipelines using AWS services including AWS Glue, Step Functions, Lambda, and Kinesis. Architect and maintain data lakes using S3, implement data cataloging with AWS Glue Catalog, and optimize data storage formats (Parquet, Delta, etc.).
Develop real-time and batch data processing solutions using Kinesis Data Streams, Kinesis Analytics, EMR, and AWS Batch. Create and maintain data models, schemas, and documentation. Build automated data quality checks, monitoring, and alerting systems.
Implement data governance policies and ensure compliance with data retention, privacy, and security requirements. Requirements US Citizenship or Green Card Holder. A Bachelor's degree or additional four(4) years of relevant experience will be needed in lieu of degree.
A minimum of Four (4) to Eight(8) years of experience in data engineering with at least 2+ years working in AWS cloud environments. Strong expertise in AWS data services, including S3, Glue, DMS, Athena, Redshift, EMR, Kinesis, and Lambda.
Proficiency in programming languages, including Python, Scala, or Java, for data processing and pipeline development. Experience with SQL and working knowledge of both relational databases (PostgreSQL, Oracle) and NoSQL systems (DynamoDB, DocumentDB). Understanding of data modeling concepts for both transactional and analytical workloads.
Experience with Infrastructure as Code tools (Terraform, CloudFormation, CDK) and CI/CD pipelines for data engineering workflows. Strong analytical and problem-solving skills with attention to data quality and system reliability.
Proven ability to work collaboratively with database administrators, data scientists, and business stakeholders. #J-18808-Ljbffr
