Senior Data Engineer – AI/ML Focus
3 semanas atrás
We're seeking a seasoned Python developer to spearhead data engineering initiatives and drive AI/ML innovation within our organization. This role is perfect for someone who's passionate about crafting scalable data pipelines, operationalizing machine learning workflows, and collaborating closely with data scientists to bring models into production. You will design, develop, and maintain cutting-edge data infrastructure that powers business-driven insights across the organization, including data models and pipelines that run through Snowflake. Key Responsibilities Develop, optimize, and maintain ETL/ELT pipelines using Python, modern data engineering frameworks, and Snowflake as a central data warehouse. Design and manage data workflows, ensuring accuracy, scalability, and reliability. Work closely with data scientists to deploy, monitor, and tune machine learning models. Create feature engineering pipelines, preprocessing workflows, and model-serving APIs. Integrate data from various sources (APIs, databases, cloud storage, streaming platforms). Implement MLOps best practices including versioning, CI/CD for ML, and automated retraining workflows. Optimize data storage, compute usage, and performance within Snowflake and cloud-native tools (AWS, GCP, or Azure). Maintain documentation, data catalogs, and operational guides. Monitor data system performance and recommend improvements. Required Skills & Qualifications 3–7+ years of experience as a Data Engineer, Python Developer, or similar backend/data role. Strong proficiency in Python, including building production-grade data pipelines. Experience with Snowflake—data modeling, Snowpipe, tasks, streams, stored procedures, and performance optimization. Experience with AI/ML workflows: feature engineering, inference pipelines, or deploying models. Proficiency in SQL and relational databases (PostgreSQL, MySQL, SQL Server). Hands-on experience with at least one cloud platform (AWS, GCP, or Azure). Experience using data orchestration tools like Airflow, Prefect, or Dagster. Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or similar. Strong understanding of data modeling, data warehousing, and distributed systems. Preferred Qualifications Experience with Spark, Databricks, or other big-data processing tools. Experience ingesting and transforming data at scale on Snowflake, including optimization of virtual warehouses. Familiarity with Kafka, Kinesis, or other streaming platforms. Understanding of CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, etc.). Exposure to deep learning frameworks (TensorFlow, PyTorch). Experience working with global clients or distributed engineering teams.
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