Databricks Data Engineer - Remote

• Design, develop, and optimize scalable data pipelines using Databricks, Apache Spark, PySpark, Python, and SQL.
• Implement enterprise lakehouse architectures using Delta Lake, Delta Live Tables, Unity Catalog, and Databricks SQL.
• Build batch and real-time data-processing solutions using technologies such as Structured Streaming, Kafka, Event Hubs, or Kinesis.
• Develop reusable ingestion and transformation frameworks for structured, semi-structured, and unstructured data.
• Design and implement medallion architectures using Bronze, Silver, and Gold data layers.
• Develop agentic AI applications capable of planning tasks, retrieving information, calling tools, executing workflows, and validating results.
• Build AI agents using frameworks such as Databricks Mosaic AI Agent Framework, LangChain, LangGraph, LlamaIndex, AutoGen, or Semantic Kernel.
• Develop retrieval-augmented generation solutions using vector embeddings, semantic search, metadata filtering, and enterprise knowledge repositories.
• Implement vector-search solutions using Databricks Vector Search or comparable vector databases.
• Integrate large language models with enterprise applications, APIs, databases, document repositories, and business workflows.
• Implement model and agent evaluation processes covering accuracy, groundedness, relevance, hallucination, safety, latency, and cost.
• Develop agent memory, prompt-management, context-management, tool-calling, guardrail, and human-in-the-loop capabilities.
• Use MLflow for experiment tracking, model registration, evaluation, deployment, and lifecycle management.
• Build and support machine-learning and generative-AI deployment pipelines using Databricks Model Serving.
• Implement CI/CD processes for notebooks, pipelines, infrastructure, models, prompts, and AI agents.
• Automate Databricks deployments using Databricks Asset Bundles, Terraform, Azure DevOps, GitHub Actions, or Jenkins.
• Establish data quality controls, reconciliation procedures, lineage, observability, monitoring, and alerting.
• Implement security and governance using Unity Catalog, role-based access controls, row-level security, column masking, secrets management, and audit logging.
• Optimize Spark workloads, cluster configurations, SQL queries, data layouts, and storage costs.
• Troubleshoot production data pipelines, AI-agent workflows, integrations, model endpoints, and performance issues.
• Collaborate with data architects, data scientists, machine-learning engineers, application developers, cybersecurity teams, and business stakeholders.
• Create technical designs, architecture diagrams, data mappings, data dictionaries, runbooks, and operational documentation.
• Mentor team members and establish engineering standards for Databricks, data pipelines, and agentic AI development.

Do you have experience in Testing and evaluation?, * 7 or more years of experience in data engineering, software engineering, or enterprise data-platform development.

• 4 or more years of hands-on experience with the Databricks platform.
• 5 or more years of experience developing solutions using Python, PySpark, Apache Spark, and SQL.
• Experience designing and implementing Databricks Lakehouse architectures.
• Experience with Delta Lake, Delta Live Tables, Databricks Workflows, Databricks SQL, and Unity Catalog.
• Experience developing batch and streaming data pipelines.
• Experience designing data models and medallion architectures.
• Hands-on experience developing generative AI, retrieval-augmented generation, or agentic AI solutions.
• Experience building AI agents that use tools, APIs, enterprise data, and multi-step reasoning workflows.
• Experience with at least one agent-development framework, such as:
• Databricks Mosaic AI Agent Framework
• LangChain
• LangGraph
• LlamaIndex
• AutoGen
• Semantic Kernel
• Experience integrating large language models from platforms such as Azure OpenAI, OpenAI, Anthropic, AWS Bedrock, Hugging Face, or open-source model providers.
• Experience with vector embeddings, vector search, semantic retrieval, document chunking, reranking, and prompt engineering.
• Experience with MLflow, model registration, model serving, and AI application evaluation.
• Experience integrating data from relational databases, REST APIs, cloud storage, event streams, SaaS applications, and document repositories.
• Experience with cloud services in at least one of the following:
• Microsoft Azure
• Amazon Web Services
• Google Cloud Platform
• Experience implementing automated deployments and CI/CD pipelines.
• Experience with data governance, security, lineage, access controls, and personally identifiable information protection.
• Strong knowledge of data-quality testing, monitoring, debugging, and performance optimization.
• Strong written and verbal communication skills., * Excellent communication skills - Very important

$65 - $68 an hour - Contract