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
Who you are:
We are building a diverse team of talented and motivated people who deeply understand business problems and enjoy solving them. You are a self-starter who loves working with data to build analytical tools that business users can leverage daily to do their jobs better. You are passionate about contributing to a growing team and establishing best practices.
As a Lead Data Engineer, you will be expected to work independently on client engagements or to lead small team project delivery, take part in the development of our practice, aid in business development, and contribute innovative ideas and initiatives to our company.
- 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
- 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 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)
- 1 or more certifications in either Databricks or Snowflake strongly 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
- Willingness to travel