Serverless Data Engineering on GCP: Simplifying ETL with Dataflow & BigQuery By Anima Acharya

Traditional ETL pipelines often come with heavy infrastructure overhead, performance bottlenecks, and scaling challenges. With Google Cloud’s serverless ecosystem, data engineers can now design pipelines that are faster to build, easier to maintain, and effortless to scale.

In this session, we’ll explore how to simplify ETL workflows using Cloud Dataflow for transformation and BigQuery as the analytical engine. You’ll learn how to ingest raw data, process it in a serverless way, and model it into analytics-ready formats without worrying about provisioning or managing infrastructure. We’ll also cover best practices around schema design and handling both batch and streaming workloads.

By the end, you’ll walk away with a practical blueprint for building modern serverless data pipelines on GCP that let you focus less on infrastructure and more on delivering insights.

About Anima Acharya:

Anima Acharya is a Data & Analytics Engineer with over four years of experience building and scaling modern data pipelines. She is passionate about transforming raw data into actionable insights using cloud-first architectures and has hands-on expertise across ingestion, modeling, and analytics. Anima is enthusiastic about Google Cloud’s data ecosystem, leveraging tools like BigQuery and Dataflow to simplify complex ETL workflows while balancing performance and cost. Beyond her technical work, she actively shares her learnings through talks and community events, with a strong focus on fostering diversity and inclusion in tech.