Lead Data Engineer

Aimpoint Digital

Lead Data Engineer building AI-ready data platforms for Aimpoint Digital’s data consultancy. Designing cloud warehouses, pipelines, semantic layers, and analytics infrastructure for client use cases.

Posted 8/25/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing AI-optimized data architectures, including cloud data warehouses and ETL/ELT pipelines. Proficient in managing stakeholder relationships and delivering scalable data solutions using modern data engineering practices.

Highest-signal resume keywords

Data Pipeline EngineeringCloud Data WarehousingSQL, Python, and Spark ProgrammingData Modeling ExpertiseDevOps Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentData ModelingSQL ProgrammingPython ProgrammingSpark ProgrammingETL/ELT ProcessesData Architecture DesignCloud Data PlatformsAI Tools UtilizationSoftware Engineering Best Practices

Soft Skills

Stakeholder ManagementCollaborationCommunication SkillsProblem SolvingTeam Leadership

Tools & Technologies

SnowflakeDatabricksDbtFivetranAWSAzureGCPDockerKubernetesApache Spark

Certifications & Qualifications

Databricks CertificationSnowflake Certification

Industry Keywords

Data EngineeringAI-Optimized Data PlatformsCloud ETL/ELT ToolsAnalytics WorkflowsConsulting Experience

Tech Stack

Tools & technologies

ApacheAWSAzureCloudDockerETLGoogle Cloud PlatformInformaticaJavaKubernetesMatillionPythonScalaSparkSQLVault

About the role

Key responsibilities & impact

Advise clients, including data owners, analytics users, and executive stakeholders, on translating business questions into AI-ready data architectures
Independently solve complex data engineering use cases across multiple industries as part of a small team
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 supporting Snowflake Cortex, Databricks Genie, BI platforms, and emerging AI copilots
Deliver scalable solutions using Snowflake, Databricks, dbt, Fivetran, and cloud-native orchestration frameworks
Engineer modern 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 SQL, Python, and Spark code using Git and CI/CD best practices
Apply AI-assisted techniques for data exploration, quality checks, schema generation, documentation, lineage, and transformation acceleration
Contribute to 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
Lead small-team project delivery, support practice development and business development, and contribute innovative ideas
Not responsible for developing machine learning models or algorithms

Requirements

What you’ll need

Degree educated in Computer Science, Engineering, Mathematics, or equivalent experience
Experience 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 production data pipelines across structured, semi-structured, and unstructured data
5+ years of data modeling, such as star schema, entity-relationship, or data vault
5+ years writing clean, maintainable, and robust code in Python, Scala, Java, or similar languages
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
Ability to manage an individual workstream independently and a small 1–2 person team
Expertise in software engineering concepts and best practices
DevOps experience required
Experience with cloud data warehouses; Databricks or Snowflake required
One or more Databricks or Snowflake certifications strongly preferred
Experience with cloud ETL/ELT tools such as Fivetran, dbt, Matillion, Informatica, or Talend preferred
Experience with cloud platforms such as AWS, Azure, or GCP and container technologies such as Docker or Kubernetes preferred
Experience with Apache Spark preferred
Experience preparing data for analytics and following a data science workflow to drive business results preferred
Consulting experience strongly preferred
Willingness to travel

Benefits

Comp & perks

Fully remote work
Opportunity to work from headquarters in Sandy Springs, GA for Atlanta applicants
Certifications to credentialize skills
Work with modern data engineering platforms and tooling
Opportunity to contribute to internal accelerators, patterns, and client-ready architectures
Opportunity to contribute innovative ideas and initiatives to the company