GCP Data Engineer

Weekday AI; Delhi; On-site; INR 600,000 - 2,600,000; Full time

Job summary

Weekday AI seeks an experienced GCP Data Engineer to design, build, and optimize scalable data pipelines on Google Cloud, leveraging BigQuery, Dataflow, and Apache Beam. You will implement ETL/ELT processes and work with Python and SQL to transform large datasets into analytics-ready structures.

You will collaborate with data architects, software engineers, and business stakeholders to deliver reliable data solutions and governance practices, while driving cost-efficient cloud architecture.

Qualifications

• Proven hands-on experience with large datasets and cloud-native data engineering services.
• Experience designing batch and streaming data pipelines on GCP (Dataflow, Beam).
• Strong SQL and Python for data transformations and testing.
• Proficient in data warehousing, dimensional modeling, and scalable schemas.
• Experience with ETL/ELT and data quality, governance, and monitoring.

Responsibilities

• Design, develop, and maintain scalable data pipelines on Google Cloud Platform.
• Build batch and streaming data pipelines using Dataflow and Apache Beam.
• Develop and optimize analytical solutions with Google BigQuery.
• Implement ETL/ELT processes for structured and semi-structured data.
• Collaborate with data architects, analysts, and BI teams to deliver data products.

Skills

GCP Data Engineering

Google Cloud

BigQuery

Dataflow

Apache Beam

Airflow

Python

SQL

Data warehousing

Data modelling

Education

Bachelor's/Master's in Computer Science, IT, Engineering, Data Science

Tools

Terraform

Dataproc

Pub/Sub

Job description

This role is for one of the Weekly's clients

Rs 600000 - Rs 2600000 (ie INR 6-26 LPA)

Experience: 6+ yrs

Location: India

Job Type: Full-time

We are looking for an experienced GCP Data Engineer with strong expertise in Google Cloud, BigQuery, Dataflow, Apache Beam, Cloud Composer, Airflow, Python, SQL, data warehousing, and data modelling.

The role involves designing, developing, and maintaining scalable data platforms and pipelines that support analytics, reporting, business intelligence, and data-driven applications. The ideal candidate will have strong hands‑on experience working with large datasets, cloud‑native data engineering services, batch and streaming pipelines, and modern data warehouse architectures.

You will work closely with data architects, analysts, software engineers, and business stakeholders to transform complex data requirements into reliable, scalable, and high-performance data solutions.

Requirements

Key Responsibilities

• Design, develop, and maintain scalable data pipelines on Google Cloud Platform (GCP).
• Build batch and streaming data pipelines using Dataflow and Apache Beam.
• Develop and optimize analytical solutions using Google BigQuery.
• Design and implement ETL/ELT processes for structured and semi-structured data.
• Develop reusable data processing frameworks using Python.
• Write complex and optimized SQL queries for data transformation, analysis, and validation.
• Build and manage workflow orchestration using Cloud Composer and Apache Airflow.
• Design DAGs, scheduling mechanisms, dependencies, retries, alerts, and error‑handling workflows.
• Develop scalable data warehouse and data modelling solutions to support reporting and analytics.
• Design fact tables, dimension tables, data marts, schemas, and analytical data structures.
• Integrate data from databases, APIs, files, applications, and other enterprise data sources.
• Implement data validation, quality checks, reconciliation, and monitoring across pipelines.
• Optimize BigQuery queries, Dataflow jobs, data models, and pipeline performance.
• Identify opportunities to improve scalability, reliability, maintainability, and cloud cost efficiency.
• Troubleshoot pipeline failures, data quality issues, performance bottlenecks, and production incidents.
• Implement appropriate data security, access controls, governance, and privacy practices.
• Collaborate with Data Architects, Data Scientists, BI Developers, Software Engineers, and business stakeholders.
• Support deployment and automation using Git, CI/CD, and modern cloud development practices.
• Maintain technical documentation covering pipelines, data models, workflows, dependencies, and operational procedures.
• Contribute to migration and modernization of legacy data platforms into GCP-based cloud data architectures.

What Makes You a Great Fit

• 6+ years of professional experience in data engineering, data platform development, or a related field.
• Strong hands‑on expertise in GCP / Google Cloud data engineering services.
• Proven experience with BigQuery for large-scale analytical workloads.
• Strong experience with Dataflow and Apache Beam for batch and streaming data processing.
• Hands‑on experience with Cloud Composer and Apache Airflow for workflow orchestration.
• Strong programming skills in Python and advanced SQL.
• Strong understanding of data warehousing, data modelling, ETL/ELT, and analytical data architectures.
• Experience designing dimensional models, fact and dimension tables, data marts, and scalable schemas.
• Good understanding of batch and real‑time data processing architectures.
• Experience working with large datasets and optimizing data processing and query performance.
• Strong knowledge of data quality, validation, monitoring, governance, and security practices.
• Experience with Git, CI/CD, automation, and production deployment workflows.
• Familiarity with other GCP services such as Cloud Storage, Pub/Sub, Dataproc, Cloud Functions, or Cloud Run is an advantage.
• Experience with Terraform or Infrastructure-as-Code is beneficial.
• Strong troubleshooting, analytical, and problem‑solving abilities.
• Excellent communication and collaboration skills across technical and business teams.
• Ability to independently own complex data engineering initiatives from design through production.
• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline is preferred.