Senior Data Engineer

dentsu Austria

GCP Data Engineer developing scalable data pipelines for AI platforms at dentsu. Collaborating with architects and analytics teams on cloud-native solutions in a fast-paced environment.

Posted 5/28/2026full-timeMumbai • 🇮🇳 IndiaSeniorWebsite

Tech Stack

Tools & technologies

AirflowBigQueryCloudETLGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Develop and maintain scalable batch and real-time data pipelines on GCP.
Build ingestion, transformation, and serving pipelines supporting enterprise analytics and AI use cases.
Assist in modernization of legacy data workflows into cloud-native architectures.
Develop reusable and maintainable data engineering components following established architectural standards.
Support implementation of event-driven and streaming-based data processing solutions.
Contribute to development of reusable and domain-oriented data products.
Implement data transformation logic and standardized data models supporting downstream analytics and AI consumption.
Support implementation of data quality validations, schema management, metadata enrichment, and reusable transformation frameworks.
Ensure data pipelines are reliable, scalable, and production-ready.
Work with GCP-native services including BigQuery, Dataflow, Dataproc, DBT, Pub/Sub, Cloud Storage, Cloud Composer (Airflow), and Cloud SQL.
Develop ETL/ELT pipelines and optimize data processing workloads.
Monitor and troubleshoot pipeline performance, failures, and operational issues.
Support implementation of semantic models and business-friendly data structures for analytics and reporting.
Collaborate with analytics and BI teams to improve consistency and usability of enterprise data assets.
Assist in development of standardized metrics, dimensions, and reusable reporting datasets.
Build and optimize AI-ready data pipelines supporting ML and GenAI initiatives.

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field.
3–6 years of experience in data engineering and cloud-based data platform development.
Hands-on experience working with Google Cloud Platform (GCP) data services.
Strong SQL and Python programming skills.
Experience developing scalable ETL/ELT pipelines and distributed data processing workflows.
Understanding of modern data architecture concepts including data lakes, data warehouses, and streaming pipelines.
Exposure to analytics, AI/ML, or GenAI-enabled data ecosystems preferred.
Strong analytical, troubleshooting, and problem-solving skills.
Ability to work collaboratively in Agile and cross-functional delivery teams.
GCP certifications such as Associate Cloud Engineer or Professional Data Engineer are a plus.

Benefits

Comp & perks

Health insurance
Professional development opportunities
Flexible work arrangements
Paid time off
Remote work options

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Hard Skills & Tools

data engineeringETLELTSQLPythondata transformationdata modelingdata quality validationstreaming data processingcloud-native architecture

Soft Skills

analytical skillstroubleshootingproblem-solvingcollaborationAgile methodology

Certifications

Associate Cloud EngineerProfessional Data Engineer