Job Title: Databricks Engineer
Location: Austin, TX
Duration: 9 Months + Extension
Bill Rate: $74/hour (C2C)
Job Type: W-2, 1099, C2C
Client: To Be Discussed Later
Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other

Job Summary:
An established organization is seeking a skilled professional to augment the internal data platform team. This role focuses on delivering robust lakehouse pipelines, catalogued engineering datasets, and enterprise data products in parallel with ongoing priority programs. The ideal candidate will quickly adapt to new domains, collaborate closely with internal leads, and contribute to standardizing Unity Catalog governance and architecture.

Key Responsibilities:

• Design and build production data pipelines on Databricks utilizing PySpark, SQL, Delta Lake, Unity Catalog, and Workflows/Jobs with high standards for monitoring and clear lineage.
• Model bronze, silver, and gold layers alongside defining data contracts, schemas, service level agreements, and quality checks.
• Develop machine learning-oriented data products, including feature stores, curated tables, embedding generation, chunking and indexing for RAG, and batch classification.
• Integrate data pipelines with cloud services, utilizing security patterns such as key vaults, managed identities, and private connectivity.
• Partner with application and machine learning teams to expose stable APIs, tables, and volumes that power orchestration workflows.
• Document data dictionaries, runbooks, and handoff materials while actively participating in code reviews and agile ceremonies.

Required Qualifications:

Education: Bachelor's degree in Computer Science, Information Systems, or a related technical field is preferred.

Experience: Minimum of 5 years of experience in data engineering, including at least 2 years of production experience with Databricks in a cloud environment.

Technical Skills: Proficiency in Python (PySpark and pandas), SQL, Delta Lake, and Unity Catalog. Practical experience with Git, continuous integration repository layouts, and unit testing for data pipelines is required.

Preferred Qualifications:

• Experience building pipelines that feed machine learning and generative AI use cases, such as vector search inputs and batch inference outputs.
• Proven ability to work effectively within staff augmentation models alongside internal technology leads.