Data Engineer (Snowflake / Databricks / BigQuery)

This is a remote position.

• Employment Type: Contract
  
• Work Mode: Remote
  
• Location: Offshore
  
• Total Experience Required: 4 to 8 years
  
• Relevant Experience Required: 3+ years of dedicated data engineering experience designing pipelines and analytics data warehouses
  
• Mandatory Certification: Snowflake Certified Core Data Engineer, Databricks Certified Data Engineer Professional, or Google Cloud Certified Professional Data Engineer
  

• Design and engineer highly scalable data ingestion pipelines to aggregate structured, semi-structured, and unstructured data streams from diverse enterprise sources (APIs, databases, application logs).
  
• Develop complex ELT/ETL data transformation models using Python, SQL, and processing frameworks (e.g., dbt, Apache Spark, PySpark).
  
• Architect and optimize enterprise cloud data platforms, designing optimized schemas, clustering keys, partition strategies, and storage parameters in Snowflake, Databricks, or BigQuery.
  
• Implement automated data orchestration pipelines, configuring workflow schedules, error-handling paths, and dependency graphs using tools like Apache Airflow, Prefect, or Mage.
  
• Establish strict data quality and validation gates, writing automated scripts to monitor data latency, validate structural constraints, check row balances, and enforce data anomaly alerts.
  
• Optimize query performance and cluster costs, auditing resource utilization footprints, restructuring inefficient SQL joins, managing micro-partitioning schemas, and tuning execution runtime bottlenecks.
  
• Govern data platform access and compliance layers, configuring fine-grained row/column-level security models, data masking rules, and access control policies (RBAC) to ensure compliance with privacy laws.
  

Requirements

• 4 to 8 years of core database engineering or backend development experience, with 3+ dedicated years actively building and maintaining enterprise-scale data infrastructure footprints.
  
• Strong technical mastery of advanced SQL optimization, Python programming, relational/dimensional data modeling (Star/Snowflake schemas, Data Vault), and cloud storage setups.
  
• Deep structural understanding of distributed computing principles, big data architectures, data stream processing constraints, and cloud resource pricing structures.
  
• Mandatory certification: Snowflake Core Data Engineer, Databricks Data Engineer Professional, or Google Cloud Professional Data Engineer.
  

• Prior experience managing large-scale legacy data warehouse migrations over to modern cloud data lakes.
  
• Familiarity with streaming architectures leveraging Apache Kafka, Flink, or cloud-native event systems (e.g., AWS Kinesis, Google Pub/Sub).