Lead Data Engineer 2026- US, COL, UK

Aimpoint Digital•Atlanta, GA

10d•Remote

About The Position

Aimpoint Digital is seeking a Lead Data Engineer to work with clients across industries to investigate complex business problems and design end-to-end analytical solutions. The role advises clients on translating business questions into AI-ready data architectures and delivers data engineering solutions that improve processes and support data-driven insights. The position is fully remote within the US and COL, with Atlanta-based applicants able to work from the Sandy Springs, GA headquarters and access to offices in London and Medellin. The Lead Data Engineer works independently on client engagements or leads small delivery teams. The role designs and implements AI-optimized data platforms, including cloud data warehouses, lakehouses, ETL/ELT pipelines, orchestration jobs, and analytical layers for LLM-driven analytics, natural-language querying, and agent-based workflows. It also builds semantic and analytical layers for platforms such as Snowflake Cortex, Databricks Genie, BI platforms, and emerging AI copilots. The role uses modern data platforms and tools to develop production-ready pipelines and data models, applying software-engineering best practices and AI-assisted engineering techniques. It contributes to Aimpoint Digital’s AI-forward data engineering practice, supports business development, and collaborates with analytics, data science, and machine-learning teams to productionize AI-enabled analytics, features, and inference pipelines. The role does not involve developing machine-learning models or algorithms.

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 working with relational databases and query languages.
At least 5 years building production data pipelines and working across structured, semi-structured, and unstructured data.
At least 5 years of data modeling experience, such as star schema, entity-relationship, or data vault.
At least 5 years writing clean, maintainable, robust code in Python, Scala, Java, or similar languages.
Ability to manage an individual workstream independently and manage a small team of 1–2 people.
Expertise in software-engineering concepts and best practices.
DevOps experience.
Experience with a cloud data warehouse; Databricks or Snowflake 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 client stakeholders, from data owners and analytics users to executives, and translate 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.
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 AI copilots.
Engineer production-ready ELT/ETL pipelines for structured, semi-structured, and unstructured data.
Design modern 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 acceleration.
Contribute internal accelerators, patterns, and client-ready architectures to the AI-forward data engineering and infrastructure practice.
Collaborate with analytics, data science, and ML project teams to productionize AI-enabled analytics, features, and inference pipelines.
Lead small project-delivery teams or manage an individual workstream independently.
Contribute to practice development, business development, and company initiatives.
Obtain certifications in relevant data platforms as part of developing and credentializing skills.