Pyspark Developer

Tata Consultancy Services; Irving (TX); On-site; USD 100,000 - 130,000; 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 in a cloud-first environment.

The role emphasizes data reliability, speed, and analytics readiness, collaborating with data scientists and analysts to translate business requirements into robust solutions. Experience with AWS/Azure/GCP data services is valued.

Qualifications

• Big data frameworks expertise with Spark architecture, drivers, executors, and DAGs.
• Advanced programming: Python and PySpark API for data transformations.
• Querying & schema management: HiveQL and ANSI SQL, with partitioning and schema definition.
• Optimized storage formats: Parquet, ORC, Avro.
• Cloud ecosystem development: AWS EMR, Azure Databricks, cloud data services.
• Data warehousing fundamentals: Star/Snowflake schemas and data lakes concepts.

Responsibilities

• Data Pipeline Development & Maintenance: Design, build, and maintain scalable ETL/ELT pipelines using PySpark and Spark SQL.
• Cloud Data Infrastructure Management: Deploy and scale data infrastructure on AWS, Azure, or GCP.
• Data Warehousing & Storage Optimization: Manage data layout, partitioning, indexing in Hive and cloud data lakes.
• Performance Tuning: Identify and resolve Spark job bottlenecks and memory issues.
• Diverse Data Integration: Ingest high-volume datasets from relational and unstructured sources.
• Automated Workflow Orchestration: Implement data workflows with Airflow or schedulers.
• Strategic Collaboration: Work with data scientists and analysts to translate requirements.

Skills

Apache Spark

PySpark API

Python

Spark SQL

HiveQL

ANSI SQL

Parquet/ORC/Avro

AWS EMR

Azure Databricks

Airflow

Data Modeling

Education

Bachelor's degree in Computer Science

Tools

AWS EMR

Azure Databricks

GCP (BigQuery)

Airflow

Job description

Job Title

Data Engineer

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 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.

Roles & Responsibilities

• Data Pipeline Development & Maintenance: Design, build, and maintain highly scalable and efficient ETL/ELT 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) (e.g., EMR, Glue), Microsoft Azure (e.g., 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 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.
• Cloud Ecosystem Development: Hands‑on development experience utilizing cloud‑native big data utilities (e.g., AWS EMR, Azure Databricks) on 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

• CI/CD & DevOps Automation: Experience with Continuous Integration/Continuous Deployment (CI/CD) 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 (e.g., AWS Certified Data Engineer, Microsoft Certified: Azure Data Engineer Associate) are highly valued.

Salary Range

$100,000 to $130,000 per year

Education

Bachelor of Computer Science