Data Engineer

Siri InfoSolutions Inc; Town of Texas (WI); On-site; USD 110,000 - 160,000; Full time

Job summary

Siri InfoSolutions Inc. is seeking a Data Engineer to design, build, and optimize scalable data pipelines using PySpark, Spark SQL, and Hive in an on-site Irving, TX role. The position focuses on reliability, speed, and efficient data processing to support BI and analytics initiatives.

You'll work with data scientists and analysts, optimize Spark jobs, and implement automated workflows with Airflow or schedulers. Strong Python, SQL, and data modeling skills are essential.

Qualifications

• Proficient in PySpark and Spark SQL for building scalable data pipelines.

Responsibilities

• Design, build, and maintain scalable ETL/ELT data pipelines using PySpark and Spark SQL.

Skills

PySpark

Spark SQL

Hive

Python

ANSI SQL

HiveQL

Parquet/ORC/Avro

Data Warehousing

Airflow

AWS EMR

Databricks

NoSQL (HBase/Cassandra/MongoDB)

CI/CD (Git/Jenkins/Ansible)

Cloud Big Data

Certifications (Spark/Data Engineer)

Tools

Airflow

Spark

Hive

Parquet

ORC

Avro

Git

Jenkins

Ansible

AWS

Databricks

Job description

Role Data Engineer

Location Irving, TX (Onsite)

Fulltime

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.

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 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 Modelling, 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 Ecosystem 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