We are looking for a Senior Data Engineer.
As a Senior Data Engineer, youll be more than just a coder – youll be the architect of our data ecosystem. Were looking for someone who can design scalable, future-proof data pipelines and connect the dots between DevOps, backend engineers, data scientists, and analysts.
Youll lead the design, build, and optimization of our data infrastructure, from real-time ingestion to supporting machine learning operations. Every choice you make will be data-driven and cost-conscious, ensuring efficiency and impact across the company.
Beyond engineering, youll be a strategic partner and problem-solver, sometimes diving into advanced analysis or data science tasks. Your work will directly shape how we deliver innovative solutions and support our growth at scale.
Responsibilities:
Design and Build Data Pipelines: Architect, build, and maintain our end-to-end data pipeline infrastructure to ensure it is scalable, reliable, and efficient.
Optimize Data Infrastructure: Manage and improve the performance and cost-effectiveness of our data systems, with a specific focus on optimizing pipelines and usage within our Snowflake data warehouse. This includes implementing FinOps best practices to monitor, analyze, and control our data-related cloud costs.
Enable Machine Learning Operations (MLOps): Develop the foundational infrastructure to streamline the deployment, management, and monitoring of our machine learning models.
Support Data Quality: Optimize ETL processes to handle large volumes of data while ensuring data quality and integrity across all our data sources.
Collaborate and Support: Work closely with data analysts and data scientists to support complex analysis, build robust data models, and contribute to the development of data governance policies.
As a Senior Data Engineer, youll be more than just a coder – youll be the architect of our data ecosystem. Were looking for someone who can design scalable, future-proof data pipelines and connect the dots between DevOps, backend engineers, data scientists, and analysts.
Youll lead the design, build, and optimization of our data infrastructure, from real-time ingestion to supporting machine learning operations. Every choice you make will be data-driven and cost-conscious, ensuring efficiency and impact across the company.
Beyond engineering, youll be a strategic partner and problem-solver, sometimes diving into advanced analysis or data science tasks. Your work will directly shape how we deliver innovative solutions and support our growth at scale.
Responsibilities:
Design and Build Data Pipelines: Architect, build, and maintain our end-to-end data pipeline infrastructure to ensure it is scalable, reliable, and efficient.
Optimize Data Infrastructure: Manage and improve the performance and cost-effectiveness of our data systems, with a specific focus on optimizing pipelines and usage within our Snowflake data warehouse. This includes implementing FinOps best practices to monitor, analyze, and control our data-related cloud costs.
Enable Machine Learning Operations (MLOps): Develop the foundational infrastructure to streamline the deployment, management, and monitoring of our machine learning models.
Support Data Quality: Optimize ETL processes to handle large volumes of data while ensuring data quality and integrity across all our data sources.
Collaborate and Support: Work closely with data analysts and data scientists to support complex analysis, build robust data models, and contribute to the development of data governance policies.
Requirements:
Bachelor's degree in Computer Science, Engineering, or a related field.
Experience: 5+ years of hands-on experience as a Data Engineer or in a similar role.
Data Expertise: Strong understanding of data warehousing concepts, including a deep familiarity with Snowflake.
Technical Skills:
Proficiency in Python and SQL.
Hands-on experience with workflow orchestration tools like Airflow.
Experience with real-time data streaming technologies like Kafka.
Familiarity with container orchestration using Kubernetes (K8s) and dependency management with Poetry.
Cloud Infrastructure: Proven experience with AWS cloud services (e.g., EC2, S3, RDS).
Bachelor's degree in Computer Science, Engineering, or a related field.
Experience: 5+ years of hands-on experience as a Data Engineer or in a similar role.
Data Expertise: Strong understanding of data warehousing concepts, including a deep familiarity with Snowflake.
Technical Skills:
Proficiency in Python and SQL.
Hands-on experience with workflow orchestration tools like Airflow.
Experience with real-time data streaming technologies like Kafka.
Familiarity with container orchestration using Kubernetes (K8s) and dependency management with Poetry.
Cloud Infrastructure: Proven experience with AWS cloud services (e.g., EC2, S3, RDS).
This position is open to all candidates.