Job
Description
Role Overview We are seeking a talented ETL Engineer to design, implement, and maintain end-to-end data ingestion and transformation pipelines in Google Cloud Platform (GCP). This role will collaborate closely with data architects, analysts, and BI developers to ensure high-quality, performant data delivery into BigQuery and downstream Power BI reporting layers. Key Responsibilities Data Ingestion & Landing Architect and implement landing zones in Cloud Storage for raw data. Manage buckets/objects and handle diverse file formats (Parquet, Avro, CSV, JSON, ORC). ETL Pipeline Development Build and orchestrate extraction, transformation, and loading workflows using Cloud Data Fusion. Leverage Data Fusion Wrangler for data cleansing, filtering, imputation, normalization, type conversion, splitting, joining, sorting, union, pivot/unpivot, and format adjustments. Data Modeling Design and maintain fact and dimension tables using Star and Snowflake schemas. Collaborate on semantic layer definitions to support downstream reporting. Load & Orchestration Load curated datasets into BigQuery across different zones (raw, staging, curated). Develop SQL-based orchestration and transformation within BigQuery (scheduled queries, scripting). Performance & Quality Optimize ETL jobs for throughput, cost, and reliability. Implement monitoring, error handling, and data quality checks. Collaboration & Documentation Work with data analysts and BI developers to understand requirements and ensure data readiness for Power BI. Maintain clear documentation of pipeline designs, data lineage, and operational runbooks. Required Skills & Experience Bachelors degree in Computer Science, Engineering, or related field. 3+ years of hands-on experience building ETL pipelines in GCP. Proficiency with Cloud Data Fusion , including Wrangler transformations. Strong command of SQL , including performance tuning in BigQuery. Experience managing Cloud Storage buckets and handling Parquet, Avro, CSV, JSON, and ORC formats. Solid understanding of dimensional modeling: fact vs. dimension tables, Star and Snowflake schemas. Familiarity with BigQuery data zones (raw, staging, curated) and dataset organization. Experience with scheduling and orchestration tools (Cloud Composer, Airflow, or BigQuery scheduled queries). Excellent problem-solving skills and attention to detail. Preferred (Good to Have) Exposure to Power BI data modeling and DAX. Experience with other GCP services (Dataflow, Dataproc). Familiarity with Git, CI/CD pipelines, and infrastructure as code (Terraform). Knowledge of Python for custom transformations or orchestration scripts. Understanding of data governance best practices and metadata management.
