Data Engineer & Data Architect - GCP / AWS / ETL / SQL / NLP / Data Warehouse

Company Description

• StarTekk’s adoption of Digital Transformation is to accelerate organizational growth, increase efficiencies and help Star Workforce achieve focused business goals. The Employee will help our organization to identify and bridge the gap that exists in the business by analyzing the data, scale and support digital transformation initiatives.

Job Description

We are seeking experienced Data Engineers, Data Architects, and ETL Testing professionals to design, develop, and maintain enterprise data platforms, data pipelines, and data warehouse solutions supporting healthcare analytics, reporting, and conversational AI applications.

The ideal candidates will have experience in cloud data engineering, data architecture, ETL/ELT development, SQL, data modeling, data validation, and knowledge pipeline engineering. Experience with GCP, AWS, NLP, and conversational AI data solutions is highly desirable.

Candidates will collaborate with data engineering, AI/ML, analytics, and business teams to deliver scalable, secure, and high-quality healthcare data solutions.

Key Responsibilities:

• Design, develop, and maintain scalable ETL/ELT data pipelines.
• Build cloud-based data engineering solutions using GCP and AWS.
• Develop and maintain data models using Star and Snowflake schemas.
• Design enterprise data warehouse architecture and data integration strategies.
• Define and implement traditional and cloud-based ETL processes.
• Develop and optimize SQL queries and data transformation workflows.
• Build data and knowledge pipelines supporting conversational AI applications.
• Prepare, transform, and optimize datasets for NLP and conversational AI use cases.
• Support conversation design operations and applied NLP data activities.
• Design and execute ETL testing strategies and test cases.
• Automate data validation and perform source-to-target testing.
• Validate data accuracy, completeness, consistency, and transformation rules.
• Identify and troubleshoot data quality and pipeline issues.
• Perform data platform performance tuning and optimization.
• Collaborate with AI/ML, data science, QA, and business teams.

Qualifications

• Data Engineering and Data Architecture.
• ETL/ELT development and data integration.
• Cloud data engineering - GCP and/or AWS.
• Advanced SQL and ANSI SQL.
• Data modeling, including Star and Snowflake schemas.
• Data warehouse architecture and development.
• Traditional and cloud-based ETL tools.
• MongoDB and Cassandra.
• Python programming and data processing.
• ETL testing and data validation.
• Test automation and scripting.
• Knowledge pipeline engineering.
• Applied NLP data processing.
• Conversational AI data engineering and conversation design operations.

Preferred Qualifications:

• Experience designing and supporting enterprise-scale data platforms.
• Experience with healthcare analytics and reporting solutions.
• Knowledge of conversational AI and NLP data workflows.
• Experience with data quality frameworks and automated testing.
• Knowledge of CI/CD practices for data platforms.
• Strong problem-solving and communication skills.
• Experience working in healthcare or other regulated environments.
• Working knowledge of HIPAA and PHI data handling.

Additional Information

Healthcare Domain Requirements:

• Understanding of healthcare data and reporting processes.
• Knowledge of HIPAA compliance requirements.
• Experience handling Protected Health Information (PHI) securely.

Additional Information:

This is an onshore healthcare technology opportunity in the United States. Candidates should have relevant hands-on experience in one or more of the above data engineering, data architecture, conversational data, or ETL testing technology areas.