Data Engineer

About The Role The role owns the design, implementation, and scaling of core data infrastructure and reliable ETL pipelines that power analytics, machine learning, and business operations across the organization. The team works closely with data scientists, product managers, and software engineers to ensure high-throughput data processing and optimal data modeling in cloud data warehouses. Key Responsibilities Design, build, and maintain scalable data pipelines and ETL workflows using Python, SQL, and modern orchestration tools like Airflow or PrefectArchitect and optimize cloud data warehouse schemas, tables, and views in Snowflake or BigQuery to ensure performant analytical queriesImplement robust data quality checks, automated monitoring, and data lineage tracking to maintain high standards of data integrityCollaborate with backend engineers to ingest event data from microservices and external APIs into central storage layersWrite clean, modular, well-tested infrastructure-as-code and data transformation logic using dbt and Git workflows What We Are Looking For 3–6 years of experience in data engineering, backend development, or analytics engineering with a focus on data pipelinesAdvanced SQL and Python programming skills, with hands-on expertise in data modeling concepts (dimensional modeling, star schema)Production experience with cloud data warehouses (Snowflake, BigQuery, or Redshift) and workflow orchestrators (Apache Airflow, Dagster)Familiarity with data governance, security best practices, and CI/CD pipelines for data engineeringBonus: Experience with streaming data technologies like Kafka or Flink, and infrastructure-as-code tools such as Terraform