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Overview

We are seeking a highly skilled Data Engineer with 6-9 years’ experience and deep expertise in both Google BigQuery and Microsoft Fabric. The ideal candidate will be responsible for building, optimizing, and maintaining data pipelines, integrating disparate data sources, and managing data workflows within a hybrid-cloud environment. This role focuses on bridging DataHub (BigQuery) with Microsoft Fabric for advanced analytics and Power BI reporting, ensuring data quality, performance, and security.  

Responsibilities

• Responsibilities
  • Data Integration & Engineering: Develop ETL/ELT pipelines to ingest, transform, and move data from Google BigQuery to Microsoft Fabric (OneLake).
  • Performance Optimization: Monitor and tune Data Factory pipelines and SQL queries for large-scale, high-performance data processing.
  • Design and develop conceptual, logical, and physical data models for large-scale data platforms and analytical systems.
  • Implement Big Data modeling techniques such as denormalization, star schema, and optimized partitioning for performance and scalability.
  • Collaborate with business stakeholders to analyze requirements, understand data flows, and translate them into robust data models.
  • Develop and maintain data models in Google BigQuery and other cloud-based environments.
  • Ensure data governance, quality checks, and compliance from a data modeling perspective.
  • Create and maintain metadata documentation and technical specifications using tools like Jira, Confluence, and SharePoint.
  • Work closely with engineering teams to support ETL/ELT processes and optimize query performance.
  • Provide mentorship and guidance to junior data modelers and analysts.
  • Participate in Agile development processes, including sprint planning and retrospectives.

Qualifications

• Qualifications
  • 6-9 years of experience in data modeling and database design.
  • Strong expertise in BigQuery and Big Data modeling techniques (denormalization, star schema, dimensional modeling).
  • Proficiency in SQL and ability to write complex queries for data analysis and validation.
  • Experience with data warehouse design methodologies (Kimball, Inmon).
  • Familiarity with cloud-based data platforms and modern data architecture principles.
  • Strong understanding of data governance, metadata management, and data quality frameworks.
  • Excellent communication, analytical, and problem-solving skills.
  Preferred Qualifications
  • Experience with large-scale data platforms and high-volume datasets.
  • Knowledge of ETL/ELT tools, data pipelines, and performance optimization techniques.
  • Exposure to Big Data technologies (e.g., Hadoop, Spark, Kafka) and streaming architectures.
  • Certifications in Google Cloud Platform (GCP) or related technologies.
  Technical Skills
  • Data Modeling: Denormalization, Star Schema, Dimensional Modeling
  • Tools: BigQuery, Erwin, ER Studio, SQLdbm
  • Cloud: Google Cloud Platform (BigQuery, Dataflow, Cloud Storage)
  • Databases: Oracle, SQL Server, MySQL, Snowflake
  • Languages: SQL, PL/SQL
  • Methodologies: Agile Scrum, Waterfall