Senior Data Engineer (GCP)

Publicis Production; Bengaluru; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Publicis Production seeks a Senior Data Engineer to design and build scalable cloud-based data solutions starting with GCP, and extending to Snowflake and Databricks. You will lead data pipeline development, ensure data quality and availability, and collaborate with teams across business and technology functions.

The role requires deep experience with Python, SQL, and multiple cloud platforms (AWS, GCP, Azure), plus hands-on work with streaming/batch pipelines, REST APIs, and DevOps practices.

Qualifications

• 5+ years of data engineering and solution delivery with leadership experience.
• Strong understanding of data modeling, warehousing concepts and distributed systems.
• Proficient in Python (PySpark), SQL and cloud-based data engineering tools.
• Experience across AWS, GCP or Azure and managing cloud data infrastructure.
• Familiarity with Databricks, Snowflake, ML pipelines and MLOps.

Responsibilities

• Architect and maintain robust data pipelines (batch and streaming) across sources (APIs, queues, files).
• Collaborate with data analysts, scientists and engineers to translate requirements into scalable solutions.
• Ensure data availability, quality and governance for production systems.
• Optimize ETL/ELT performance and scalability of data infrastructure.
• Implement monitoring, logging and data quality checks across pipelines.

Skills

Python (PySpark)

SQL & data modeling

Cloud platforms (AWS, GCP, Azure)

Databricks & Snowflake

REST APIs & web scraping

Git & CI/CD

Data governance & quality

Tools

Git

CI/CD

Azure DevOps

REST API clients

Job description

We are seeking a proactive and self-motivated Senior Data Engineer with a proven track record in building scalable cloud-based data solutions across multiple cloud platforms to support our work in architecting, building and maintaining the data infrastructure. The specific focus for this role will start with GCP however we require experience with Snowflake and Databricks also.

As a senior member within the data engineering space, you will play a pivotal role in designing scalable data pipelines, optimizing data workflows, and ensuring data availability and quality for production technology.

The ideal candidate brings deep technical expertise in AWS, GCP and/or Databricks alongside essential hands-on experience building pipelines in Python, analysing data requirements with SQL, and modern data engineering practices. Your ability to work across business and technology functions, drive strategic initiatives, and independently problem solve will be key to success in this role.

Qualifications:

Experience:

• 5+ years of experience in data engineering and solution delivery, with a strong track record of technical leadership.
• Deep understanding of data modeling, data warehousing concepts, and distributed systems.
• Excellent problem-solving skills and ability to progress with design, build and validate output data independently.
• Deep proficiency in Python (including PySpark), SQL, and cloud-based data engineering tools.
• Expertise in multiple cloud platforms (AWS, GCP, or Azure) and managing cloud-based data infrastructure.
• Familiarity with machine learning pipelines and MLOps practices.
• Additional experience with Databricks and specific AWS such as Glue, S3, Lambda
• Proficient in Git, CI/CD pipelines, and DevOps tools (e.g., Azure DevOps)
• Hands-on experience with web scraping, REST API integrations, and streaming data pipelines.

Key Responsibilities:

• Architect and maintain robust data pipelines (batch and streaming) integrating internal and external data sources (APIs, structured streaming, message queues etc.).
• Collaborate with data analysts, scientists, and software engineers to understand data needs and develop solutions.
• Understand requirements from operations and product to ensure data and reporting needs are met
• Implement data quality checks, data governance practices, and monitoring systems to ensure reliable and trustworthy data.
• Optimize performance of ETL/ELT workflows and improve infrastructure scalability.

Working Conditions:

• Full-time.
• Limited travel may be required depending on team needs.
• Flexibility to operate in a global organization.