Data engineer • austin tx
Job Description:<br /> <b>Key Responsibilities:</b></p> <p> </p> <p> <b>Architect and Develop</b>: Contribute to the platform's archit... Show more
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GCP Data Engineer to serve fraud data mart customers.Data Engineer will closely work with cross-functional teams to ensure data integrity, reliability, and scalability.You need to have expertise in... Show more
Role Descriptions:</strong></p> <p><br /> • Hands-on experience with Alibaba Cloud data services.Design, build, and maintain scalable data platforms on public cloud , m... Show more
The average salary range is between $ 115,000 and $ 174,673 year , with the average salary hovering around $ 136,500 year .
The average salary range is between $ 105,570 and $ 172,017 year , with the average salary hovering around $ 130,000 year .
Job Description:
Key Responsibilities:
Architect and Develop: Contribute to the platform's architectural design and build integration, modelling, data persistence, and analytical systems.
Data Pipelines: Implement, maintain, and test robust data pipelines.
Metadata Management: Develop and manage metadata processes and tools.
Performance Monitoring: Ensure the stability and performance of data pipelines.
Data Quality: Implement tools for data curation, metadata management, and quality assurance.
Collaboration: Engage with business and technology teams to align the platform with organizational goals.
Preferred Technical Skills:
Programming: 5+ years of experience in Python/Java.
Cloud Expertise: Strong understanding of AWS services (e.g., Lambda, Step Functions, ECS).
Data Platforms: Hands-on experience with Snowflake and data stack technologies like Apache Iceberg and Spark.
Workflow Orchestration: Exposure to tools like Apache Airflow, Prefect, Dagster, or DBT.
Data Services: Familiarity with AWS Glue, Lake Formation, EMR, EventBridge, Athena, and similar services.
Metadata Tools: Experience with tools like Amundsen, Atlas, DataHub, OpenDataDiscovery, or Marquez.
RDBMS: Knowledge of PostgreSQL is a plus.
Industry Experience: Proven experience building enterprise-wide data and analytics systems, preferably in financial services or asset management.
