Scala+AWS Engineer

Tata Consultancy Services; Hyderabad, Bengaluru; On-site; INR 1,500,000 - 2,200,000; Full time

Job summary

Tata Consultancy Services is seeking a Spark / Scala Data Engineer to design, develop, and maintain scalable data solutions on the AWS Cloud Platform. You will collaborate with business stakeholders, data architects, and data scientists to deliver high-performance data platforms.

The role emphasizes modern data pipelines, data warehousing, and cloud-native analytics, plus migration of Scala/Spark workloads to PySpark and cloud modernization initiatives.

Qualifications

• Experience designing scalable data pipelines in AWS using Scala and PySpark.
• Strong data warehousing, big data processing, and cloud-native analytics experience.
• Ability to collaborate with stakeholders, data architects, and data scientists.

Responsibilities

• Design and optimize scalable data pipelines in AWS with Scala and PySpark.
• Build batch and real-time data ingestion and processing frameworks.
• Develop enterprise data warehousing solutions using Scala and PySpark.
• Migrate Scala Spark apps to PySpark and optimize performance.
• Implement ETL/ELT processes for structured and unstructured data.
• Support CI/CD and IaC for deployment automation.
• Collaborate with data teams to translate requirements into technical solutions.
• Lead migration and optimization of Spark workloads on cloud.

Skills

Scala

PySpark

AWS

Databricks

Snowflake

ETL/ELT

CI/CD

IaC

Spark

Data warehousing

Tools

Databricks

Snowflake

Job description

Spark / Scala Data Engineer

Job Requirements:

We are seeking an experienced Scala / Pyspark associate to design, develop, and maintain scalable data solutions on the Amazon Cloud Platform (AWS). The ideal candidate will have strong expertise in building modern data pipelines, data warehousing, big data processing, and cloud-native analytics solutions. The role requires close collaboration with business stakeholders, data architects, data scientists, and application teams to deliver reliable and high-performance data platforms.

Key Responsibilities:

• Design, develop, and optimize scalable data pipelines using scala and Pyspark in AWS environment.
• Build and maintain batch and real-time data ingestion and processing frameworks.
• Develop enterprise-grade data warehousing solutions using Scala and Pyspark.
• Analyze existing Scala and Spark applications and identify migration requirements.
• Convert Scala-based ETL, batch, and streaming pipelines into PySpark frameworks.
• Optimize PySpark jobs for performance, scalability, and resource utilization.
• Support cloud modernization initiatives on AWS/Databricks/Snowflake platforms.
• Implement ETL/ELT processes for structured and unstructured data.

• Design, develop, and optimize scalable data pipelines using scala and Pyspark in AWS environment.
• Build and maintain batch and real-time data ingestion and processing frameworks.
• Develop enterprise-grade data warehousing solutions using Scala and Pyspark.
• Analyze existing Scala and Spark applications and identify migration requirements.
• Convert Scala-based ETL, batch, and streaming pipelines into PySpark frameworks.
• Optimize PySpark jobs for performance, scalability, and resource utilization.
• Support cloud modernization initiatives on AWS/Databricks/Snowflake platforms.
• Implement ETL/ELT processes for structured and unstructured data.
• Integrate data from multiple sources including databases, APIs, files, and streaming platforms.
• Ensure data quality, governance, security, and compliance across data platforms.
• Automate deployment and operational processes using CI/CD and Infrastructure as Code (IaC).
• Monitor data pipelines and troubleshoot production issues.
• Collaborate with Data Architects, Business Analysts, and Data Scientists to translate business requirements into technical solutions.
• Implement data models, metadata management, and data lineage best practices.
• Support migration of on-premises or multi-cloud data platforms to AWS.
• Lead the migration, modernization, and optimization of Scala and Apache Spark workloads on Cloud environment, including conversion of Scala-based Spark applications to PySpark, performance tuning, cluster optimization, dependency management, and ensuring scalable, cost-effective, and resilient data processing solutions.