Lead Data Engineer
New
A
Aimpoint DigitalData engineering consulting
This position is fully-remote within the US and COLFull-TimeLead
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLJavaSnowflakeDevOpsScalaData modelingdbtDatabricks
Requirements
- Degree in Computer Science, Engineering, Mathematics, or equivalent experience.
- 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 experience, such as star schema, entity-relationship, or data vault.
- 5+ years writing clean, maintainable code in Python, Scala, Java, or similar languages.
- Experience with Databricks or Snowflake cloud data warehouses.
- DevOps experience and expertise in software engineering concepts and best practices.
- Ability to manage an individual workstream independently and lead a small team of 1–2 people.
- Experience managing stakeholders and collaborating with customers.
- Willingness to travel.
- Preferred: experience with dbt Core/Cloud, AI engineering tools, cloud ETL/ELT tools, cloud platforms, containers, Apache Spark, and consulting.
- Preferred: at least one Databricks or Snowflake certification.
Responsibilities
- Advise client stakeholders on AI-ready data architectures and translate business questions into data solutions.
- Design and implement cloud data warehouses, lakehouses, ETL/ELT pipelines, orchestration jobs, and analytical layers.
- Build semantic and analytical layers for Snowflake Cortex, Databricks Genie, BI platforms, and AI copilots.
- Engineer pipelines for structured, semi-structured, and unstructured data.
- Design data models, including metrics layers, knowledge graphs, and semantic models.
- Write production-ready SQL, Python, and Spark code using Git and CI/CD practices.
- Apply AI-assisted techniques to data exploration, quality checks, schema generation, documentation, lineage, and transformation.
- Collaborate with analytics, data science, and ML teams to productionize AI-enabled analytics, features, and inference pipelines.
- Lead client workstreams or small project teams and contribute to internal accelerators, architectures, and practice development.
View Full Description & ApplyYou'll be redirected to the employer's site
