Senior Data AI Developer

Education & Experience

• Bachelor's degree in computer science, Information Systems, Data Analytics, Engineering, or a related technical field.

• 5+ years of experience designing and developing enterprise data, analytics, or BI solutions.

• 3+ years of hands-on experience developing Power BI enterprise reporting solutions.

• Experience in design and development of data integration, ETL/ELT, and data warehousing

• Experience with Microsoft Azure data services, Microsoft Fabric, or similar cloud data platforms.

• Experience developing AI-enabled applications using Python, Azure OpenAI, Large Language Models (LLMs), or machine learning technologies is preferred.

• Strong analytical, problem-solving, and communication skills, with the ability to collaborate effectively with business and technical teams.

Technical Skills

Data & Analytics

• Data Modeling

• Data Warehousing

• Data Lakehouse

• ETL/ELT

• SSIS, REST API's

• Azure Data Factory

• Azure Data Lake Storage (ADLS)

• Azure DevOps, Git, CI/CD

• Power BI

• DAX

• Power Query

• Semantic Models

• DevOps, Git, CI/CD

AI & Machine Learning

• Python
• scikit-learn
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• ML frameworks (PyTorch, TensorFlow, scikit-learn)

Benefits/Salary:

The position includes competitive wages and a comprehensive benefits package available day one of hire, including health insurance (medical, dental, vision), excellent 401k matching program, paid holidays and vacation. Howmet Aerospace curates a strong working environment through opportunities for career progression, community engagement activities, and flexible schedules contingent upon role and location. The salary range for this position is $95-$120k

We are seeking a Senior Data & AI Developer to design, build, and support enterprise data, analytics, and AI solutions. This role will develop scalable data pipelines, enterprise data models, Power BI solutions, and AI-enabled applications that transform business data into actionable insights and automation opportunities.

The ideal candidate combines strong data engineering and analytics skills with hands-on AI experience to identify business opportunities, develop proofs-of-concept, and deliver solutions using modern data and AI technologies.

Key Responsibilities

Data & Analytics

• Design, develop, and support scalable Data Lake, Lakehouse, Data Warehouse, and AI-enabled data platforms across cloud and hybrid environments.

• Design and develop ETL/ELT pipelines using Azure Data Factory, Microsoft Fabric, or similar technologies.

• Design scalable data models and integration patterns to support enterprise analytics and AI initiatives.

• Establish event-driven, streaming, batch, and API-based integration patterns across enterprise systems.

• Build and maintain data integration pipelines for ERP, CRM, PLM, MES, and other enterprise applications.

• Implement data validation, monitoring, quality controls, and data governance best practices.

• Design, develop, and maintain enterprise Power BI reports, dashboards, and scorecards.

• Develop reusable semantic models, datasets, and data products that enable self-service analytics.

• Optimize Power BI performance, data refresh processes, and security, including Row-Level Security (RLS) and workspace governance.

• Collaborate with business stakeholders to define reporting requirements, KPIs, and analytics solutions.

• Optimize SQL queries and data processing performance to ensure scalable and efficient data solutions.

• Evaluate emerging data and AI technologies and recommend solutions that improve business processes and operational efficiency.

AI & Automation

• Evaluate emerging data and AI technologies and recommend solutions that improve business processes and operational efficiency.

• Partner with business stakeholders to identify, prioritize, and deliver AI use cases that improve productivity, decision-making, and operational efficiency.

• Integrate AI capabilities with enterprise applications and data platforms using APIs and Python.

• Create AI proofs-of-concept and transition successful solutions into production.

• Develop AI-powered solutions using Azure OpenAI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG).

• Design and implement AI agents and agentic workflows where appropriate.

• Establish best practices for AI security, governance, and responsible AI usage.