5-7 years of experience as a Data Engineer with a focus on PySpark and Apache Spark technologies.
Strong programming skills in Python, particularly with the PySpark API.
Proficient in HiveQL and ANSI SQL along with solid schema management skills.
Expertise in big data storage formats like Parquet, ORC, and Avro.
Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud.
Understanding of Data Lake concepts and experience in dimensional modeling.
Responsibilities
Design and maintain scalable ETL/ELT pipelines using PySpark and Spark SQL.
Deploy and manage cloud data infrastructure on platforms like AWS or Azure.
Optimize data layout and performance in Apache Hive and cloud data lakes.
Identify and fix performance issues in Spark jobs using Spark UI.
Develop solutions to integrate diverse datasets into the data ecosystem.
Implement automated workflows with tools like Apache Airflow for reliable data delivery.
Collaborate with data scientists and analysts to meet business data needs.
Benefits
Opportunity to work on cutting-edge big data technologies.
Collaborative work environment with cross-functional teams.
Exposure to cloud-native data infrastructure management.
Professional development opportunities, including certifications.
Flexible work arrangements in a dynamic office setting.
Full Job Description
Role description
Job Title: Pyspark Developer
Work Location : Irving, Texas
Job Summary
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 a dynamic cloud environment 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
Key Responsibilities
Data Pipeline Development Maintenance Design build and maintain highly scalable and efficient ETLELT data pipelines utilizing PySpark and Spark SQL for complex data transformations
Cloud Data Infrastructure Management Deploy manage and scale critical data infrastructure components on leading cloud platforms such as Amazon Web Services AWS eg EMR Glue Microsoft Azure eg Databricks Synapse or Google Cloud Platform GCP
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 indepth analysis effectively managing data skewness and optimizing memory utilization
Diverse Data Integration Develop robust solutions for ingesting highvolume 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 industrystandard scheduling tools like Apache Airflow or platformnative schedulers ensuring timely and reliable data delivery
Strategic Collaboration Partner closely with data scientists business analysts and crossfunctional enterprise teams to translate complex business requirements into technically sound and efficient data solutions
Required Core Technical Skills
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 Indepth understanding and practical experience with optimized big data storage file formats such as Parquet ORC and Avro
Cloud Ecosystem Development Handson development experience utilizing cloudnative big data utilities eg AWS EMR Azure Databricks within major cloud platforms
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
CICD DevOps Automation Experience with Continuous IntegrationContinuous Deployment CICD practices and automation tools like Git Jenkins or Ansible
NoSQL Database Integration Exposure to and experience with NoSQL databases such as HBase Cassandra or MongoDB
Professional Cloud Certifications Relevant professional cloud certifications eg AWS Certified Data Engineer Microsoft Certified Azure Data Engineer Associate are highly valued
