Data Engineer

Raas Infotek LLC

Plano, United States

1 day 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

www.dice.com

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