What You'll Do
Design, implement, and maintain robust, scalable data pipelines for batch and real-time processing using Spark, and other modern tools.
Own the backend data infrastructure, including ingestion, transformation, validation, and orchestration of large-scale datasets.
Leverage Google Cloud Platform (GCP) services to architect and operate scalable, secure, and cost-effective data solutions across the pipeline lifecycle.
Develop and optimize ETL/ELT workflows across multiple environments to support internal applications, analytics, and machine learning workflows.
Build and maintain data marts and data models with a focus on performance, data quality, and long-term maintainability.
Collaborate with cross-functional teams including development teams, product managers, and external stakeholders to understand and translate data requirements into scalable solutions.
Help drive architectural decisions around distributed data processing, pipeline reliability, and scalability.
4+ years in backend data engineering or infrastructure-focused software development.
Proficient in Python, with experience building production-grade data services.
Solid understanding of SQL
Proven track record designing and operating scalable, low-latency data pipelines (batch and streaming).
Experience building and maintaining data platforms, including lakes, pipelines, and developer tooling.
Familiar with orchestration tools like Airflow, and modern CI/CD practices.
Comfortable working in cloud-native environments (AWS, GCP), including containerization (e.g., Docker, Kubernetes).
Bonus: Experience working with GCP
Bonus: Experience with data quality monitoring and alerting
Bonus: Experience with Snowflake, DBT, Flink, Kafka
Bonus: Strong hands-on experience with Spark for distributed data processing at scale.
Degree in Computer Science, Engineering, or related field.
