Lead Data Engineer

Summary

Join our team as a Lead Data Engineer and lead cloud modernization initiatives, develop scalable data pipelines, and enable real-time data processing. You will drive the transformation of legacy infrastructure into a robust, cloud-native data ecosystem. This high-impact role requires expertise in Google Cloud Platform (GCP) and BigQuery. Key responsibilities include data migration, integration, ETL/data pipeline development, and query optimization. We offer competitive compensation and benefits, remote flexibility, and growth opportunities. The ideal candidate will have 5+ years of data engineering experience with a strong focus on cloud and big data technologies and proven experience migrating on-premise data systems to the cloud.

Requirements

• 5+ years in Data Engineering with a strong focus on cloud and big data technologies
• Minimum 2+ years of hands-on experience with GCP, specifically BigQuery
• Proven experience migrating on-premise data systems to the cloud
• Strong development experience with Apache Airflow, Python, and Apache Spark
• Expertise in streaming data ingestion, particularly in IoT or sensor data environments
• Strong SQL development skills; experience with BigQuery performance tuning
• Solid understanding of cloud architecture, data modeling, and data warehouse design
• Familiarity with Git and CI/CD practices for managing data pipelines

Responsibilities

• Analyze legacy on-premises and hybrid cloud data warehouse environments (e.g., SQL Server)
• Lead the migration of large-scale datasets to Google BigQuery
• Design and implement data migration strategies ensuring data quality, integrity, and performance
• Integrate data from various structured and unstructured sources, including APIs, relational databases, and IoT devices
• Build real-time streaming pipelines for large-scale ingestion and processing of IoT and telemetry data
• Modernize and refactor legacy SSIS packages into cloud-native ETL pipelines
• Develop scalable, reliable workflows using Apache Airflow, Python, Spark, and GCP-native tools
• Ensure high-performance data transformation and loading into BigQuery for analytical use cases
• Write and optimize complex SQL queries, stored procedures, and scheduled jobs within BigQuery
• Develop modular, reusable transformation scripts using Python, Java, Spark, and SQL
• Continuously monitor and optimize query performance and cost efficiency in the cloud data environment

Preferred Qualifications

• GCP Professional Data Engineer certification
• Experience with modern data stack tools like dbt, Kafka, or Terraform
• Exposure to ML pipelines, analytics engineering, or DataOps/DevOps methodologies

Benefits

• Competitive compensation and benefits
• Remote flexibility and growth opportunities