Job description

Duration: 12 months Job Description: • Candidates should be comfortable working in a hybrid environment and collaborating with teams across multiple locations. Responsibilities: • Ability to lead hands-on development efforts while providing technical direction, code reviews, and engineering oversight across multiple initiatives • Experience establishing development environments, infrastructure standards, security controls, and migration strategies across multiple AWS accounts and environments • Ability to architect, develop, and govern enterprise-scale data pipelines, ETL processes, data ingestion frameworks, and orchestration workflows • Proven ability to identify data gaps, define strategic remediation plans, and implement scalable automation solutions that improve analytical capabilities across the organization • Ability to design and implement highly reliable data pipelines with strong focus on data quality, observability, resiliency, and operational supportability • Experience building and optimizing large-scale data warehousing solutions that prioritize both business user experience and system performance • Ability to drive technical decision-making, influence architectural direction, and effectively communicate complex technical concepts to both technical and non-technical stakeholders • Proven track record mentoring engineers, fostering technical growth, and building high-performing data engineering teams • Experience leading cross-functional initiatives involving data engineering, analytics, architecture, platform engineering, and business stakeholders • Strong problem-solving and leadership skills with the ability to manage competing priorities in fast-paced enterprise environment Experience: • Core Experience and Abilities as described in the job description. Skillsets: • Proficiency in Python, SQL, Spark, and Generative AI technologies for scalable data and analytics solutions • Hands-on experience with AWS, Clienture, Databricks, and cloud-based AI/data platforms • Knowledge of LLMs, Retrieval-Augmented Generation (RAG), vector databases, and AI-powered search solutions • Strong understanding of responsible AI, data governance, data quality, security, and AI compliance practices Qualifications : • Experience implementing enterprise data governance and metadata management platforms • Experience with real-time analytics, event-driven architectures, and streaming data platforms • Knowledge of modern data architecture patterns including Data Mesh, Lakehouse, and domain-oriented design • Experience leading large-scale cloud transformation or enterprise data modernization programs • Experience creating AI applications using AWS Bedrock or similar Generative AI platforms • Experience building AI-ready data pipelines and ML workflows, including feature engineering and MLOps Certifications: • AWS Certified Data Engineer, AWS Solutions Architect, or equivalent cloud certifications US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit www.ustechsolutions.com . US Tech Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. AI Statement: By applying, you acknowledge that AI-assisted tools may be used during hiring. #LI-AS140

Requirements

Must-haves

Duration: 12 months
Candidates should be comfortable working in a hybrid environment and collaborating with teams across multiple locations.
Ability to lead hands-on development efforts while providing technical direction, code reviews, and engineering oversight across multiple initiatives
Experience establishing development environments, infrastructure standards, security controls, and migration strategies across multiple AWS accounts and environments
Ability to architect, develop, and govern enterprise-scale data pipelines, ETL processes, data ingestion frameworks, and orchestration workflows
Proven ability to identify data gaps, define strategic remediation plans, and implement scalable automation solutions that improve analytical capabilities across the organization
Ability to design and implement highly reliable data pipelines with strong focus on data quality, observability, resiliency, and operational supportability
Experience building and optimizing large-scale data warehousing solutions that prioritize both business user experience and system performance
Ability to drive technical decision-making, influence architectural direction, and effectively communicate complex technical concepts to both technical and non-technical stakeholders
Proven track record mentoring engineers, fostering technical growth, and building high-performing data engineering teams
Experience leading cross-functional initiatives involving data engineering, analytics, architecture, platform engineering, and business stakeholders
Strong problem-solving and leadership skills with the ability to manage competing priorities in fast-paced enterprise environment
Core Experience and Abilities as described in the job description.
Proficiency in Python, SQL, Spark, and Generative AI technologies for scalable data and analytics solutions
Hands-on experience with AWS, Clienture, Databricks, and cloud-based AI/data platforms
Knowledge of LLMs, Retrieval-Augmented Generation (RAG), vector databases, and AI-powered search solutions
Strong understanding of responsible AI, data governance, data quality, security, and AI compliance practices
Experience implementing enterprise data governance and metadata management platforms
Experience with real-time analytics, event-driven architectures, and streaming data platforms
Knowledge of modern data architecture patterns including Data Mesh, Lakehouse, and domain-oriented design
Experience leading large-scale cloud transformation or enterprise data modernization programs
Experience creating AI applications using AWS Bedrock or similar Generative AI platforms
Experience building AI-ready data pipelines and ML workflows, including feature engineering and MLOps
AWS Certified Data Engineer, AWS Solutions Architect, or equivalent cloud certifications

Nice-to-haves

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Application prep

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