Senior Engineer 2

Job Summary:

YoE: 5-10

We are looking for a Senior Data Engineer with a strong background in machine learning infrastructure, data pipeline development, and collaboration with data scientists to drive the deployment and scalability of advanced analytics and AI solutions. You will play a pivotal role in building and optimizing data systems that power ML models, dashboards, and strategic insights across the company.

• Design, develop, and optimize scalable data pipelines and ETL/ELT processes to support ML workflows and analytics.
• Collaborate with data scientists to operationalize machine learning models in production environments (batch, real-time).
• Build and maintain data lakes, data warehouses, and feature stores using modern cloud technologies (e.g., AWS/GCP/Azure, Snowflake, Databricks).
• Implement and maintain ML infrastructure, including model versioning, CI/CD for ML, and monitoring tools (MLflow, Airflow, Kubeflow, etc.).
• Develop and enforce data quality, governance, and security standards.
• Troubleshoot data issues and support the lifecycle of model development to deployment.
• Partner with software engineers and DevOps teams to ensure data systems are robust, scalable, and secure.
• Mentor junior engineers and provide technical leadership on data and ML infrastructure.

Required:

• 5+ years of experience in data engineering, ML infrastructure, or a related field.
• Proficient in Python, SQL, and big data processing frameworks (Spark, Flink, or similar).
• Experience with orchestration tools like Apache Airflow, Prefect, or Luigi.
• Hands-on experience deploying and managing machine learning models in production.
• Deep knowledge of cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
• Familiarity with CI/CD tools for data and ML pipelines.
• Experience with version control, testing, and reproducibility in data workflows.

Preferred:

• Experience with feature stores (e.g., Feast), ML experiment tracking (e.g., MLflow), and monitoring solutions.
• Background in supporting NLP, computer vision, or time-series ML models.
• Strong communication skills and ability to work cross-functionally with data scientists, analysts, and engineers.
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.