Tata Consultancy Services

Tata Consultancy Services

Engineer

Irving, TX · Senior

Sponsorship not specified$70k-$80kDetected 99 days ago
PythonGitSQLNoSQLCassandraSnowflakeDatabricksAWSGCPAzureCloud PlatformsCI/CDJenkinsDevOpsSparkAirflowData EngineeringAI OrchestrationEHR/EMRCollaboration

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odds of building a lasting career here

37Risky
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level I)39
Fits your clock70

Thin sponsorship signal and lottery-bound (~15% per draw). A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.

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Employer immigration record

from this employer's Department of Labor filings

Third-party placement is common here

32% of this employer's visa filings place the worker at a client company rather than at the employer itself, across 424 clients. Your visa, salary band and layoff exposure would follow the employer of record, not the client.Most common: CITIBANK N.A · BANK OF AMERICA · HUMANA INCFiles as an H-1B dependent employer.

Reported a layoff in this occupation

In a green-card filing decided 2026-03-12, this employer stated it had laid off workers in Software Quality Assurance Analysts and Testers (NY) during the preceding six months. 2 such filings on record.Describes the period before that filing, not necessarily today. Unlike WARN notices this is occupation-specific and federal.

Files H-1B transfers

135 transfer filings in the last year, covering 135 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • 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.
  • 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.

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.
  • 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.
  • Design, build, and maintain highly scalable and efficient ETL/ELT data pipelines utilizing PySpark and Spark SQL for complex data transformations.
  • 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).
  • Strategically manage data layout, partitioning, and indexing within Apache Hive and various cloud data lake solutions to optimize performance and accessibility.

Requirements

  • 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) with in 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.
  • Big Data Frameworks Expertise:
  • Demonstrated high proficiency in Apache Spark architecture, including a deep understanding of drivers, executors, and Directed Acyclic Graphs (DAGs).
  • Exceptional coding skills in Python and extensive experience with the PySpark API for developing intricate data transformations and processing logic.
  • Strong command of HiveQL and ANSI SQL, coupled with expertise in data partitioning techniques and effective schema definition.

Nice to have

  • 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
  • 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:

Skills

  • Data Integration

Compensation

  • $70,000 to $80,000 per year

Company info

  • 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.
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This listing is sourced directly from Tata Consultancy Services's careers page and normalized into a canonical job model.