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Senior Data Engineer - Enterprise AI & Cloud Data Platform

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AI Summary

Design, develop, and optimize scalable cloud-based data pipelines and platforms for enterprise AI initiatives. Lead technical teams and mentor engineers while ensuring data quality, governance, and performance. Requires 10+ years of data engineering experience with Python, SQL, and cloud technologies.

Key Highlights
Senior Data Engineer role supporting Enterprise AI initiatives in Shale & Tight business
12+ month contract with W2 employment only, visa sponsorship available
10+ years experience required with Python, SQL, AWS/Azure/GCP, and distributed processing
Key Responsibilities
Design, develop, and optimize scalable cloud-based data pipelines and platforms
Translate complex business requirements into technical solutions
Partner with Software Engineers, AI Engineers, Data Scientists, and business stakeholders to deliver enterprise AI data solutions
Lead the design and implementation of scalable, secure, and maintainable data architectures
Improve existing data infrastructure through automation and engineering best practices
Ensure data quality, governance, reliability, and platform performance
Troubleshoot production issues and perform root cause analysis
Mentor junior and mid-level Data Engineers while providing technical leadership
Drive engineering standards, documentation, and code quality
Technical Skills Required
Python SQL AWS
Benefits & Perks
Visa Sponsorship
W2 Employment

Job Description


STRATEGIC STAFFING SOLUTIONS HAS AN OPENING!

This is a Contract Opportunity with our company that MUST be worked on a W2 Only. No C2C eligibility for this position. Visa Sponsorship is Available! The details are below.

“Beware of scams. S3 never asks for money during its onboarding process.”


Job Title: Data Engineer

Location: Houston, TX

Contract Length: 12+ Months

Pay: 80-90 per hr on W2


Seeking a Senior Data Engineer to support Enterprise AI initiatives within the Shale & Tight business. This is a senior, hands-on engineering role responsible for designing, developing, and optimizing scalable cloud-based data platforms and pipelines that enable AI, analytics, and machine learning solutions.

The ideal candidate can work independently, solve complex technical problems, provide technical leadership, mentor engineers, and partner with Software Engineers, AI Engineers, Data Scientists, and business stakeholders to deliver enterprise-scale data solutions.

Required Qualifications

  • Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field (or equivalent experience)
  • 10+ years of Data Engineering experience
  • Expert-level Python and SQL development
  • Experience designing and building scalable ETL/ELT data pipelines
  • Strong experience with AWS, Azure, or GCP
  • Experience with distributed data processing technologies such as Spark or Databricks
  • Experience with data modeling, data architecture, and data integration
  • Experience with orchestration tools such as Airflow, Azure Data Factory (ADF), or AWS Glue
  • Experience working with both structured and unstructured data
  • Experience with CI/CD, Git, and DevOps best practices
  • Proven experience designing enterprise-scale data platforms and pipelines
  • Experience building data solutions supporting analytics, AI, or machine learning workloads
  • Strong troubleshooting and root cause analysis skills
  • Demonstrated experience providing technical leadership and mentoring Data Engineers
  • Excellent verbal and written communication skills
  • Ability to work independently, prioritize competing initiatives, and thrive in ambiguous environments

Key Responsibilities

  • Design, develop, and optimize scalable cloud-based data pipelines and platforms
  • Translate complex business requirements into technical solutions
  • Partner with Software Engineers, AI Engineers, Data Scientists, and business stakeholders to deliver enterprise AI data solutions
  • Lead the design and implementation of scalable, secure, and maintainable data architectures
  • Improve existing data infrastructure through automation and engineering best practices
  • Ensure data quality, governance, reliability, and platform performance
  • Troubleshoot production issues and perform root cause analysis
  • Mentor junior and mid-level Data Engineers while providing technical leadership
  • Drive engineering standards, documentation, and code quality


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