AI-Enabled Data Engineer

📋 Description

• Design, build, and maintain scalable data pipelines across cloud and on-prem.
• Work with Snowflake, Databricks, and Delta Lake as primary data platforms.
• Model data with dbt: modular SQL, manage dependencies, and contracts.
• Build semantic layers for governed metrics used by downstream consumers.
• Design data warehouse schemas and data lakes for performance and cost.
• Implement data quality: testing, validation, alerting, and lineage in pipelines.

🎯 Requirements

• ETL/ELT design, data modeling, quality/testing, lineage, batch & incremental loads.
• Snowflake, Databricks, Spark/PySpark, Delta Lake, data warehouses and data lakes.
• dbt Core / dbt Cloud, SQL advanced, semantic layer, dimensional modeling.
• Airflow, Dagster/Prefect, Azure Data Factory, Databricks Workflows.
• RAG & vector pipelines, AI-augmented ETL, MCP/agent tools, LLM integration.
• AWS S3/Glue/Redshift; Azure ADLS/ADF/Synapse; CI/CD for data; IaC.

🎁 Benefits

• Work on real, complex data problems across client environments.
• A team that takes AI tooling seriously and expects you to use it.
• Access to the full modern data stack — no one-tool shops.
• Room to grow into data architecture, platform leadership, or AI engineering.
• Collaborative culture that values opinions, craft, and intellectual curiosity.