Data Engineer (Cloudera Platform)

Job Role: Data Engineer (Cloudera Platform)
Job Location: Irving, TX (Onsite)
Job Type: Full Time
Salary Range: $100000 to $130000/Annum Full Time Benefits

Job Description:
We are seeking a highly skilled and motivated Data Engineer to play a pivotal role in designing, building, and Eligible to workimizing our next-generation scalable data pipelines. This position requires expertise in processing massive datasets using cutting-edge technologies like Apache Spark, PySpark, and Hive within Cloudera Platform. Your primary objective will be to ensure the utmost data reliability, speed, and efficiency, providing a robust foundation for downstream business intelligence and advanced analytics initiatives.

Roles & Responsibilities:
" Data Pipeline Development & Maintenance: Design, build, and maintain highly scalable and efficient ETL/ELT data pipelines utilizing PySpark and Spark SQL , Hive for complex data transformations.
" Data Warehousing & Storage Eligible to workimization: Strategically manage data layout, partitioning, and indexing within Apache Hive and various cloud data lake solutions to Eligible to workimize performance and accessibility.
" Performance Tuning & Eligible to workimization: Proactively identify and resolve performance bottlenecks in Spark jobs, leveraging Spark UI for in-depth analysis, effectively managing data skewness, and Eligible to workimizing memory utilization.
" Diverse Data Integration: Develop robust solutions for ingesting high-volume and diverse datasets from both structured relational databases and unstructured flat files into our data ecosystem.
" Automated Workflow Orchestration: Implement and manage automated data workflows using industry-standard scheduling tools like Apache Airflow or platform-native schedulers, ensuring timely and reliable data delivery.
" Strategic Collaboration: Partner closely with data scientists, business analysts, and cross-functional enterprise teams to translate complex business requirements into technically sound and efficient data solutions.
Qualifications:
" Big Data Frameworks Expertise: Demonstrated high proficiency in Apache Spark architecture, including a deep understanding of drivers, executors, and Directed Acyclic Graphs (DAGs).
" Advanced Programming: Exceptional coding skills in Python and extensive experience with the PySpark API for developing intricate data transformations and processing logic.
" Querying & Schema Management: Strong command of HiveQL and ANSI SQL, coupled with expertise in data partitioning techniques and effective schema definition.
" Eligible to workimized Storage Formats: In-depth understanding and practical experience with Eligible to workimized big data storage file formats such as Parquet, ORC, and Avro.
" Data Warehousing Fundamentals: Solid foundation in Dimensional Data Modeling, including Star and Snowflake schemas, and practical experience with Data Lakes concepts and implementation.
Preferred Qualifications
" CI/CD & DevOps Automation: Experience with Continuous Integration/Continuous Deployment (CI/CD) practices and automation tools like Git, Jenkins, or Ansible.
" Cloud Ecosyste m Development: Experience in development experience utilizing cloud-native big data utilities (e.g., AWS EMR, AWS Databricks) within major cloud platforms.
" NoSQL Database Integration: Exposure to and experience with NoSQL databases such as HBase, Cassandra, or MongoDB.
" Professional Certifications: Relevant professional certifications on Spark or Data Engineer are highly valued