Data Scientist (Big Data Engineer) II – Only Local to TEXAS

Position: Data Scientist (Big Data Engineer) II
Openings: 2
Location: 100% Remote - Only Local to TEXAS
Duration: 12 Months ( Up to 3 Years extension )
Rate : $70/C2C

Position Overview

The Texas Department of Family and Protective Services (Texas DFPS) is seeking experienced Data Scientist (Big Data Engineer) II professionals to support data engineering, machine learning, and analytics initiatives involving large-scale data processing.

The selected candidates will be responsible for developing, maintaining, and optimizing scalable big data solutions using the Databricks Unified Analytics Platform and Microsoft Azure.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Apache Spark on Databricks.

• Implement ETL/ELT workflows for structured and unstructured data.

• Develop and optimize Spark jobs for performance and cost efficiency.

• Build and maintain data models, schemas, and database structures supporting analytical and operational use cases.

• Integrate Databricks solutions with Azure Data Factory and other Azure cloud services.

• Work with Azure Data Lake Storage and data warehouse solutions.

• Implement data validation and quality checks to ensure data accuracy, consistency, and reliability.

• Contribute to data governance initiatives, including metadata management, data lineage, and data cataloging.

• Implement data security measures, including encryption, access controls, and auditing.

• Support compliance with applicable regulations, security requirements, and industry best practices.

• Automate deployments using CI/CD pipelines, DevOps practices, and version control systems.

• Work with Databricks notebooks, clusters, jobs, and Delta Lake.

• Utilize Unity Catalog and/or Delta Lake to support data quality, governance, and security.

• Troubleshoot and debug data pipelines, Spark applications, and related technical issues.

• Collaborate with data scientists, data analysts, stakeholders, and cross-functional teams.

• Work effectively within Agile and multicultural environments.

Required Qualifications

• 4+ years of experience implementing ETL/ELT workflows for structured and unstructured data.

• 4+ years of experience automating deployments using CI/CD tools.

• 4+ years collaborating with data scientists, analysts, stakeholders, and cross-functional teams.

• 4+ years designing and maintaining data models, schemas, and database structures.

• 4+ years working with data storage solutions, including Azure Data Lake Storage and data warehouses.

• 4+ years implementing data validation and data quality checks.

• 4+ years contributing to data governance, metadata management, data lineage, and data cataloging.

• 4+ years implementing data security measures, including encryption, access controls, and auditing.

• 4+ years of proficiency in Python and R programming languages.

• 4+ years of strong SQL querying and data manipulation experience.

• 4+ years of experience with the Microsoft Azure cloud platform.

• 4+ years of experience with DevOps, CI/CD pipelines, and version control systems.

• 4+ years working in Agile and multicultural environments.

• 4+ years of strong troubleshooting and debugging capabilities.

• 3+ years designing and developing scalable data pipelines using Apache Spark on Databricks.

• 3+ years optimizing Spark jobs for performance and cost efficiency.

• 3+ years integrating Databricks with Azure Data Factory.

• 3+ years ensuring data quality, governance, and security using Unity Catalog or Delta Lake.

• 3+ years of strong understanding of Apache Spark architecture, RDDs, DataFrames, and Spark SQL.

• 3+ years of hands-on experience with Databricks notebooks, clusters, jobs, and Delta Lake.

Preferred Qualifications

• Knowledge of machine learning libraries such as:

  • MLflow

  • Scikit-learn

  • TensorFlow

• Databricks Certified Associate Developer for Apache Spark certification.

• Microsoft Certified: Azure Data Engineer Associate certification.

Core Technical Skills

Databricks | Apache Spark | PySpark | Spark SQL | Python | R | SQL | Azure | Azure Data Factory | Azure Data Lake Storage | Delta Lake | Unity Catalog | ETL/ELT | Data Pipelines | Data Warehousing | CI/CD | DevOps | Data Governance | Data Quality | Data Security

Ideal Candidate Profile

The ideal candidate will be a hands-on Databricks/Azure Big Data Engineer with strong experience building and optimizing Spark-based data pipelines, implementing ETL/ELT processes, working with Azure data services, and supporting data governance, quality, security, and CI/CD initiatives.

Candidates should demonstrate recent hands-on Databricks and Apache Spark experience, rather than having only general Azure or data engineering experience.