Data Warehousing & BigQuery Development

Design and implement data warehouse solutions using Google BigQuery

Develop, optimize, and maintain complex SQL queries and data models

Create efficient partitioning, clustering, and indexing strategies

Data Pipeline Development

Build scalable data pipelines using GCP services (Dataflow, Dataproc, Cloud Composer)

Perform data ingestion from various sources (APIs, databases, cloud storage)

Ensure data transformation, cleansing, and validation

Performance Optimization

Optimize BigQuery queries for cost and performance

Monitor query execution and tune workloads

Implement best practices for storage and compute efficiency

Integration & ETL/ELT

Implement ETL/ELT workflows using BigQuery, Cloud Storage, Pub/Sub

Integrate data from multiple cloud and on-prem systems

Automate workflows and scheduling

Collaboration & Reporting

Work closely with data analysts, data scientists, and business teams

Support reporting tools like Looker, Data Studio (Looker Studio), Tableau

Translate business requirements into scalable data solutions

Required Skills & Qualifications

Core Technical Skills

3–5 years of experience in data engineering / BigQuery development

Strong expertise in SQL (mandatory)

Hands-on experience with Google BigQuery

Experience with data modeling (star/snowflake schemas)

GCP Tools & Services

BigQuery

Cloud Storage

Dataflow / Dataproc

Pub/Sub

Cloud Composer (Airflow)

Programming Skills

Proficiency in Python / SQL scripting

Experience with Spark (PySpark) is a plus

• Primary skills:Technology->Cloud Platform->GCP, BIG QUERY

Experience with real-time data streaming

Knowledge of data governance, security, and IAM in GCP

Familiarity with CI/CD pipelines and DevOps practices

Certification in Google Cloud (e.g., Data Engineer Associate/Professional)