Data Engineer

About The Role

The role owns the design, implementation, and scaling of core data infrastructure, high-throughput ETL pipelines, and analytical data models that drive company-wide decision-making.

The team works closely with data scientists, product managers, and analytics engineers to ensure data reliability, performance, and accessibility across all business units.

Key Responsibilities

• Architect, build, and maintain scalable batch and streaming data pipelines using Python, SQL, and Apache Spark
• Manage cloud data warehousing infrastructure (Snowflake, BigQuery, or Redshift) with a focus on cost optimization and query performance
• Implement robust data quality checks, schema evolution handling, and monitoring tools to guarantee data integrity across all sources
• Collaborate with analytics and engineering teams to model data for reporting, machine learning features, and operational workflows
• Write clean, modular, and well-tested infrastructure-as-code using Terraform and CI/CD deployment pipelines
• Document data lineage, schemas, and pipeline architectures to foster a culture of data transparency and self-service
  
  

What We Are Looking For

• 3–6 years of professional experience in data engineering, backend development, or analytics engineering roles
• Advanced SQL and Python programming skills, with proven expertise in building production-grade data pipelines
• Hands-on experience with modern cloud data warehouses (Snowflake, BigQuery) and orchestrators (Airflow, Dagster, Prefect)
• Solid understanding of distributed data processing frameworks such as Apache Spark, Flink, or Ray
• Bachelor's degree in Computer Science, Statistics, Engineering, or equivalent practical experience
• Bonus: Experience with real-time streaming technologies (Kafka, Kinesis) and data lakehouse formats (Iceberg, Delta Lake)