Data Engineer
Raas Infotek LLC
Plano, United States
about 2 months ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours
Job source
Tech stack
Amazon Web Services
Microsoft Azure
Big Data
Cloud Database
Continuous Integration
Information Engineering
Data Integration
Extract Transform Load (ETL)
Data Warehousing
Python (Programming Language)
Operational Databases
Cloud Services
+20 more
SQL Databases
Data Processing
Freeform SQL
Google Cloud
Cloud Platform System
Azure Data Factory
Snowflake
Apache Spark
Git
Data Lakes
Pyspark
Kubernetes
AWS Glue
Apache Kafka
Data Management
Data Lakehouse
Video Streaming
Data Pipelines
Docker
Databricks
Job description
- Design and develop scalable data pipelines and ETL/ELT processes
- Build and maintain cloud-based data platforms
- Develop complex SQL queries and optimize data-processing workloads
- Implement data integration across multiple sources
- Develop batch and streaming data solutions
- Design data models, data warehouses, and lakehouse architectures
- Ensure data quality, reliability, security, and governance
- Collaborate with architects, analysts, developers, and business stakeholders
- Troubleshoot production data issues and improve pipeline performance
- Mentor junior and mid-level data engineers
- Support modernization and migration of legacy data platforms to cloud environments
Requirements
We are seeking a highly experienced Senior Data Engineer with 12+ years of expertise in designing, developing, and optimizing scalable data platforms, pipelines, and cloud-based data solutions., * 12+ years of experience in Data Engineering
- Strong expertise in Python, SQL, ETL/ELT
- Advanced experience with AWS / Azure / Google Cloud Platform
- Hands-on experience with Snowflake, Databricks, or equivalent cloud data platforms
- Strong knowledge of data warehousing and dimensional data modeling
- Experience with Spark / PySpark
- Expertise in Azure Data Factory / AWS Glue / similar orchestration tools
- Experience developing batch and real-time data pipelines
- Strong knowledge of Kafka or other streaming technologies
- Experience with Data Lake / Data Lakehouse architectures
- Strong understanding of data quality, validation, governance, and security
- Experience with CI/CD, Git, Docker, and Kubernetes
- Performance tuning and optimization of large-scale data workloads
- Excellent communication and stakeholder-management skills
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