AWS data lead

Tata Consultancy Services; Pune City; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

A leading company is seeking an AWS Data Senior to join their team in Pune. The role involves backend development using AWS services, particularly Glue and Lambda, alongside troubleshooting and deployment responsibilities. Ideal candidates will have extensive experience in AWS and data management solutions, contributing to the development and maintenance of data pipelines.

Qualifications

• 7 to 11 years of total IT experience.
• Knowledge of broad AWS Services and backend development.

Responsibilities

• Write AWS Glue/Lambda jobs for data sourcing to Data Lake.
• Troubleshoot production issues and create deployment plans.

Skills

AWS

PySpark

Python

Data Management

DevOps

Job description

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Student at Indian Academy School Of Management Studies

Job Title

AWS Data Senior

Total IT Experience (in Yrs.)

7 to 11 ; Location - Pune

Key words to search in resume

AWS, glue, S3, ETL Developer, Pyspark Developer

Technical/Functional Skills - MUST HAVE SKILLS

• Knowledge of broad AWS Services, experience in backend development/maintenance preferably using PySpark/Python, awareness of AWS cost calculations, troubleshooting abilities.

Secondary Skills

Data management solutions with capabilities such as metadata and catalog management, performance and capacity optimization, data security, data modeling, data wrangling, DevOps, and basic agile awareness.

Responsibilities

• Write AWS Glue/Lambda jobs for sourcing data from multiple source systems to Data Lake. Conduct unit testing of ingested data across landing, staging, and curated layers.
• Analyze source system documents and configure environments for dev, QA, UAT, and production deployments.
• Troubleshoot production issues that arise from builds.
• Create detailed deployment plans and coordinate with the infrastructure team (Terraform) for code and configuration deployment.
• Hands-on experience with AWS Glue, Lambda, S3, IAM roles, and Hadoop ecosystem components like DBFS, Parquet, Delta Tables, HDFS, MapReduce.
• Deep understanding of Spark architecture including Spark Core, SQL, DataFrames, Streaming, RDD caching, and MLlib.
• Proficiency in scripting languages such as Python and PySpark.
• Experience in SDLC phases—analysis, design, coding, testing—following best practices.
• Expertise in Spark SQL with data sources like JSON, Parquet, and key-value pairs.
• Experience in creating tables, partitioning, bucketing, and data aggregation using Spark SQL/Scala.

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Information Technology

Industries

• IT Services and IT Consulting

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