Lead Data Engineer 2026- US, COL

Aimpoint Digital•Atlanta, GA

1d•Remote

About The Position

Aimpoint Digital is a fully remote AI and data consultancy that works with clients across industries to solve complex business problems. The Lead Data Engineer designs and delivers end-to-end analytical solutions and AI-ready data architectures, serving as a trusted data and AI advisor to client stakeholders, from data owners and analytics users to executives. The role works independently or leads small project teams to build scalable data platforms, pipelines, models, and analytical layers that enable data-driven insights and AI-enabled workflows. The position uses modern data engineering platforms and tools, including Snowflake, Databricks, dbt, Fivetran, and cloud-native orchestration frameworks. It applies AI-assisted engineering techniques to activities such as data exploration, quality checks, schema generation, documentation, lineage, and transformation. The Lead Data Engineer also contributes to Aimpoint Digital’s AI-forward engineering practice, internal accelerators, client architectures, business development, and the development of team best practices. The role collaborates with analytics, data science, and machine learning teams to productionize AI-enabled analytics, features, and inference pipelines; it does not involve developing machine learning models or algorithms. The role is fully remote within the US and COL. Atlanta-based applicants may also work from the company’s new headquarters in Sandy Springs, Georgia. Travel is expected.

Requirements

Degree in Computer Science, Engineering, Mathematics, or equivalent experience.
Experience managing stakeholders and collaborating with customers.
Strong written and verbal communication skills.
At least 5 years of experience with relational databases and query languages.
At least 5 years of experience building production data pipelines and working with structured, semi-structured, and unstructured data.
At least 5 years of data modeling experience, such as star schema, entity-relationship, or data vault modeling.
At least 5 years of experience writing clean, maintainable, robust code in Python, Scala, Java, or similar languages.
Ability to manage an individual workstream independently and lead a small team of 1–2 people.
Expertise in software engineering concepts and best practices.
DevOps experience.
Experience with cloud data warehouses; Databricks or Snowflake experience is required.
Willingness to travel.

Nice To Haves

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

Responsibilities

Advise clients on data and AI, translating business questions into AI-ready data architectures.
Work independently or as part of a small team to solve complex data engineering use cases across industries.
Lead small project teams and manage individual workstreams independently.
Design and implement AI-optimized data platforms, including cloud data warehouses, lakehouses, ETL/ELT pipelines, orchestration jobs, and analytical layers.
Build and evolve semantic and analytical layers for Snowflake Cortex, Databricks Genie, BI platforms, and emerging AI copilots.
Engineer production ELT/ETL pipelines for structured, semi-structured, and unstructured data.
Design data models emphasizing metrics layers, knowledge graphs, and semantic consistency for AI consumption.
Write production-ready code in SQL, Python, and Spark, following software engineering practices such as Git and CI/CD.
Apply AI-assisted data engineering techniques to data exploration, quality checks, schema generation, documentation, lineage, and transformation.
Contribute to the evolution of the AI-forward data engineering and infrastructure practice, including internal accelerators, patterns, and client-ready architectures.
Collaborate with analytics, data science, and ML teams to productionize AI-enabled analytics, features, and inference pipelines.
Contribute to practice development, business development, best practices, and innovative company initiatives.
Travel as needed.