Key skills
PythonSQLETLELTData EngineeringAnalyticsBusiness IntelligenceSnowflakeDatabricksSparkKafkaAirflowdbtAWSTerraformKubernetesDockerCI/CDLeadership
About this role
We are seeking an experienced Data Engineer to design, build, and optimize scalable data platforms that support analytics, reporting, and data-driven decision-making across the organization. This role will partner closely with Product, Analytics, Data Science, and Engineering teams to develop reliable, high-performance data solutions and drive best practices in modern data engineering.
Key Responsibilities
- Design and maintain scalable ELT/ETL pipelines using SQL, Python, dbt, and cloud-native technologies.
- Build and optimize data warehouse and lakehouse solutions leveraging Snowflake, Databricks, and AWS services.
- Develop dimensional data models and analytics-ready datasets to support business intelligence and advanced analytics.
- Implement data quality, monitoring, and observability frameworks to ensure data reliability and trust.
- Build and support batch and near-real-time data pipelines using Kafka, Spark, and Airflow.
- Optimize platform performance, scalability, and cost across data infrastructure and workloads.
- Collaborate with cross-functional stakeholders to translate business requirements into scalable data solutions.
- Drive CI/CD, infrastructure-as-code, and engineering best practices across the data platform.
- Mentor junior engineers and contribute to technical leadership, architecture decisions, and code reviews.
Required Skills
- 8+ years of experience in Data Engineering or related fields.
- Advanced SQL and Python development experience.
- Strong expertise with Snowflake, dbt, Airflow, and modern ELT frameworks.
- Experience designing dimensional models and enterprise-scale data warehouses.
- Hands-on experience with Kafka, Spark/PySpark, and streaming data architectures.
- Strong understanding of cloud platforms, particularly AWS.
- Experience with Terraform, Docker, Kubernetes, and CI/CD pipelines.
- Knowledge of data governance, schema evolution, data quality, and observability practices.
Qualifications
- Experience supporting analytics, product, marketplace, ecommerce, or customer data platforms.
- Exposure to Databricks and multi-cloud environments.
- Background partnering with Data Science, Product, and Analytics teams.
- Master''s degree in Mathematics, Computer Science, Engineering, or a related quantitative field.