Data Engineer

• Build and optimize data ingestion pipelines on Databricks (batch and streaming) to process structured, semi-structured, and unstructured data.
• Implement scalable data models and transformations leveraging Delta Lake and open data formats (Parquet, Delta).
• Design and manage workflows with Databricks Workflows, Airflow, or equivalent orchestration tools.
• Implement automated testing, lineage, and monitoring frameworks using tools like Great Expectations and Unity Catalog.
• Build integrations with enterprise and third-party systems via cloud APIs, Kafka/Kinesis, and connectors into Databricks.
• Partner with AI/ML teams to provision feature stores, integrate vector databases (Pinecone, Milvus, Weaviate), and support RAG-style architectures.
• Optimize Spark and SQL workloads for speed and cost efficiency across multi-cloud environments (AWS, Azure, GCP).
• Apply secure-by-design data engineering practices aligned with Point Wild’s cybersecurity standards and evolving post-quantum cryptographic frameworks.
• Collaborate closely with data architects, AI engineers, and product leaders to deliver a scalable, resilient, and secure foundation for analytics and ML.

Data Engineer

Data Engineer

Data Engineer II

Senior Data Engineer

Lead Data Engineer

Lead Data Engineer