AWS Data Engineer

Tata Consultancy Services; Irving (TX); On-site; USD 90,000 - 110,000; Full time

Job summary

Tata Consultancy Services in Irving, TX is seeking a Data Engineer to design, build, and optimize scalable data pipelines using Spark, PySpark, and Hive on the Cloudera platform. The role focuses on reliability, speed, and efficiency to support BI and advanced analytics initiatives.

You will develop ETL/ELT processes, optimize storage with Hive and cloud data lakes, and collaborate with data scientists and analysts to translate business requirements into robust data solutions.

Qualifications

• Big Data Frameworks Expertise: Apache Spark architecture, drivers, executors, and DAGs.
• Advanced Programming: Python and PySpark for complex data transformations.
• Querying & Schema Management: HiveQL and ANSI SQL with partitioning and schema definition.
• Optimized Storage Formats: Parquet, ORC, and Avro proficiency.
• Data Warehousing Fundamentals: Dimensional modeling and Data Lakes concepts.

Responsibilities

• Data Pipeline Development & Maintenance: Design, build, and maintain scalable ETL/ELT pipelines with PySpark and Spark SQL, Hive.
• Data Warehousing & Storage Optimization: Manage data layout, partitioning, and indexing in Hive and cloud data lakes.
• Performance Tuning & Optimization: Identify bottlenecks in Spark jobs, analyze with Spark UI, manage data skew and memory.
• Diverse Data Integration: Ingest high-volume structured and unstructured data into the data ecosystem.
• Automated Workflow Orchestration: Use Airflow or platform schedulers to ensure timely data delivery.
• Strategic Collaboration: Work with data scientists, analysts, and cross-functional teams to translate requirements.

Skills

Apache Spark

PySpark

HiveQL

ANSI SQL

Python

Education

Bachelor's degree in Computer Science

Tools

Git

Jenkins

AWS EMR

Databricks

HBase

MongoDB

Job description

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.

Job Description

Roles & Responsibilities

Job Title: Data Engineer

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

Salary Range: $90,000 to $110,000 per year

Qualifications: BACHELOR OF COMPUTER SCIENCE