Design scalable data solutions and build robust data pipelines.
Integrate machine learning models into data workflows.
Collaborate with cross-functional teams to enhance data processing efficiencies.
Manage ETL processes within cloud environments.
Optimize and troubleshoot existing data systems and processes.

3+years of hands-on experience in data engineering with a focus on ETL workflows, data pipelines, and cloud computing.
Strong experience with AWS services for data processing and storage (e.g., S3, Glue, Athena, Lambda, Redshift).
Proficiency in programming languages such as Python, PySpark
Deep understanding of microservices architecture and distributed systems.
Familiarity with AI/ML tools and frameworks (e.g., TensorFlow, PyTorch) and their integration into data pipelines.
Experience with big data technologies like Apache Spark, Kafka, and Snowflake.
Strong problem-solving and performance optimization skills.
Exposure to modern DevOps practices, including CI/CD pipelines and container orchestration tools like Docker and Kubernetes.
Experience working in agile environments delivering complex data engineering solutions.
Proven expertise or certification in Palantir Foundry is highly preferred.

Data Engineer

Key skills