Data Engineer (Cloudera Platform)

Prism IT Global; Irving (TX); On-site; USD 120,000 - 150,000; Full time

Job summary

Prism IT Global is seeking a Data Engineer to design, build, and optimize scalable data pipelines using Apache Spark, PySpark, and Hive within Cloudera/AWS environments.

You will develop ETL/ELT processes, manage data warehousing structures, and ensure high performance through tuning, partitioning, and efficient storage formats. Collaboration with data scientists and analytics teams is essential.

Qualifications

• Proficiency with Spark architecture, drivers, executors and DAGs.
• Strong Python programming with PySpark for data transformations.
• Experience with HiveQL/ANSI SQL and partitioning strategies.
• Knowledge of optimized big data formats (Parquet/ORC/Avro).
• Solid understanding of dimensional modeling and data warehouse concepts.

Responsibilities

• Design, build and maintain scalable ETL/ELT pipelines using PySpark and Spark SQL/Hive.
• Optimize data layout, partitioning and indexing for performance.
• Tune Spark jobs and manage memory to reduce skew and bottlenecks.
• Ingest high-volume structured and unstructured data into the data ecosystem.
• Implement automated workflows with Airflow or similar schedulers.
• Collaborate with data scientists and analysts to translate requirements into data solutions.

Skills

Apache Spark

PySpark

HiveQL

Python

Data modeling

ETL

Tools

Parquet/ORC/Avro

Spark SQL

Hive

Airflow

AWS EMR

Databricks

Job description

Location: Irving TX

In-Person Drive for below requirement on Thursday (Aug 27th)

Job Description

We are seeking a highly skilled and motivated Data Engineer to play a pivotal role in designing, building, and optimizing 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.

• 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 Optimization: Strategically manage data layout, partitioning, and indexing within Apache Hive and various cloud data lake solutions to optimize performance and accessibility.
• Performance Tuning & Optimization: Proactively identify and resolve performance bottlenecks in Spark jobs, leveraging Spark UI for in-depth analysis, effectively managing data skewness, and optimizing 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.
• Optimized Storage Formats: In-depth understanding and practical experience with optimized 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