Data Engineer

dentsu Austria

. Design and build scalable ETL/ELT pipelines on AWS .

Posted 10/7/2026full-timePune • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building scalable ETL/ELT pipelines on AWS, utilizing strong SQL and Python skills for data transformations and pipeline development. Proficient in implementing data ingestion pipelines and optimizing data models for analytics and performance.

Highest-signal resume keywords

AWS Data ServicesSQL Data TransformationsPython Data EngineeringData ModelingInfrastructure as Code

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ETL PipelinesData IngestionData TransformationData ModelingSQLPythonPySparkAWS GlueAmazon RedshiftAmazon EMR

Soft Skills

Communication SkillsCollaboration

Tools & Technologies

AWS S3AWS GlueAmazon AthenaCloudWatchTerraformCloudFormation

Certifications & Qualifications

AWS Certified Solutions ArchitectAWS Certified DevOps – ProfessionalSnowflake Core Certification

Industry Keywords

Data LakesData Warehouse ArchitecturesBatch Data PipelinesStreaming ConceptsDistributed Data Processing

Tech Stack

Tools & technologies

Amazon RedshiftAWSCloudETLPySparkPythonSparkSQLTerraform

About the role

Key responsibilities & impact

Design and build scalable ETL/ELT pipelines on AWS
Develop SQL-based data transformations and Python-based data pipelines
Implement data ingestion pipelines using AWS services such as S3, Glue, and EMR
Build data models optimized for analytics, performance, and cost efficiency
Support deployment and execution of data pipelines across environments
Monitor pipeline performance, reliability, and data quality
Troubleshoot data pipeline issues and perform root-cause analysis
Apply best practices for security, reliability, and scalability
Work closely with architects and product teams to understand requirements
Translate business and analytics needs into working AWS data solutions
Contribute to documentation, code reviews, and engineering standards
Serve as an individual contributor focused on data pipeline development, cloud data engineering, and analytics enablement in a global delivery environment

Requirements

What you’ll need

Minimum 3 years and maximum up to 7 years of experience
Strong hands-on experience with AWS data services, including Amazon S3, AWS Glue, Amazon Athena, Amazon Redshift, and Amazon EMR
Experience designing cloud-native data lakes and data warehouse architectures
Solid understanding of batch data pipelines and basic exposure to streaming concepts
Strong SQL skills, including complex queries, joins, aggregations, and transformations
Experience working with large datasets in Redshift/Athena
Strong Python skills for data engineering and ETL use cases
Experience with PySpark/Spark is a strong plus
Good understanding of data modeling, transformations, and performance tuning
Hands-on experience with distributed data processing frameworks such as Spark/PySpark
Experience handling structured and semi-structured data
Understanding of schema evolution, data quality checks, and validation logic
Working knowledge of Infrastructure as Code using Terraform and/or CloudFormation
Basic experience with CI/CD pipelines for data workloads
Understanding of logging and monitoring using CloudWatch
Ability to work closely with architects, DevOps, QA, and business stakeholders
Good communication skills to explain technical concepts clearly
Bachelor's or Master's degree or equivalent degree
AWS Certified Solutions Architect / DevOps – Professional certification listed
Snowflake Core certification listed