Designs and optimizes scalable data architectures, ingestion pipelines, ELT/ETL transformations, cloud data warehouses, and orchestration workflows. The role uses Python, SQL, dbt, Spark, and platforms such as Snowflake, Databricks, and BigQuery. Responsibilities include data quality validation, query and cost optimization, dimensional modeling, access controls, data masking, compliance, and enterprise data warehouse migrations. Familiarity with streaming technologies and mandatory certification in Snowflake, Databricks, or Google Cloud is required.
This is a remote position.
Data Engineer (Snowflake / Databricks / BigQuery)
Job Details
- 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
Job Summary
We are seeking an experienced Data Engineer to design, build, and optimize high-throughput data architectures and transformation pipelines. The ideal candidate will orchestrate scalable ELT/ETL processes, manage massive relational database models, optimize query workloads within enterprise cloud data lakes, and deliver clean, structured data layers to power downstream business intelligence and data science models.
Key Responsibilities
- 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.
Preferred Qualifications
- 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).
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