Lead Data Engineer 2026- US

Aimpoint Digital•Atlanta, GA

10d•Remote

About The Position

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

Requirements

Degree educated in Computer Science, Engineering, Mathematics, or equivalent experience
Experience with managing stakeholders and collaborating with customers
Strong written and verbal communication skills required
5+ years working with relational databases and query languages
5+ years building data pipelines in production and ability to work across structured, semi-structured and unstructured data
5+ years data modeling (e.g. star schema, entity-relationship, data vault)
5+ years writing clean, maintainable, and robust code in Python, Scala, Java, or similar coding languages
Ability to manage an individual workstream independently and ability to manage a small 1-2 person team
Expertise in software engineering concepts and best practices
DevOps experience required
Experience working with cloud data warehouses (Databricks or Snowflake required)

Nice To Haves

5+ years’ experience with dbt Core/Cloud preferred
Experience enabling or accelerating data platform engineering workflows with AI tools such as Codex, Claude, Copilot, Snowflake Cortex Code, and/or Databricks Genie Code preferred
Experience working with cloud ETL/ELT tools (Fivetran, dbt, Matillion, Informatica, Talend, etc.) preferred
Experience working with cloud platforms (AWS, Azure, GCP) and container technologies (Docker, Kubernetes) preferred
Experience working with Apache Spark preferred
Experience preparing data for analytics and following a data science workflow to drive business results preferred
Consulting experience strongly preferred
1 or more certifications in either Databricks or Snowflake strongly preferred

Responsibilities

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