Lead Data Engineer 2026- US

Do you enjoy working with clients from different industries to investigate complex business problems and to design end-to-end analytical solutions that will improve their existing processes and ability to derive data-driven insights?

Aimpoint Digital is a dynamic and fully remote AI and Data consultancy.

We work alongside the most innovative software providers in the data engineering space to solve our clients' toughest business problems.

At Aimpoint Digital, we believe in blending modern tools and techniques with tried-and-true principles to deliver optimal data engineering solutions.

You will:

• Become a trusted data and AI advisor to our clients, from data owners and analytic users to executive stake holders helping them translate business questions into AI-ready data architectures
• Work independently as part of a small team to solve complex data engineering use-cases across a variety of industries
• Design and implement AI-optimized data platforms, including cloud data warehouses, lakehouses, ETL/ELT pipelines, orchestration jobs, and analytic layers that support LLM-driven analytics, natural language querying, and agent-based workflows
• Build and evolve semantic and analytical layers that power tools like Snowflake Cortex, Databricks Genie, BI platforms, and emerging AI Copilots
• Use modern platforms and tooling such as Snowflake, Databricks, dbt, Fivetran, and cloud-native orchestration frameworks to deliver scalable solutions, and credentialize your skills with certifications
• Engineer modern ELT/ETL pipelines that handle structured, semi-structured, and unstructured data to support AI and analytics use cases
• Design modern data models with an emphasis on metrics layers, knowledge graphs, and semantic consistency for AI consumption
• Write production-ready code in SQL, Python, and Spark, following software engineering tools and best-practices such as Git and CI/CD
• Apply AI-assisted data engineering techniques for data exploration, quality checks, schema generation, documentation, lineage, and transformation acceleration
• Contribute to the evolution of our AI-forward data engineering and infrastructure practice, including internal accelerators, patterns, and client-ready architectures
• Collaborate with analytics, data science, and ML project teams to productionize AI-enabled analytics, features, and inference pipelines. Note: You will not be developing machine learning models or algorithms