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Data Engineer

AHEAD$150K – $180K per yearUnited States only

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The Data Engineer, Data Platform will build and operate the data capabilities that help AHEAD teams access trusted, usable, and well-managed information. This role will develop ingestion pipelines, transformations, data models, and curated data products in the modern cloud data platform, with an emphasis on Snowflake and dbt.

The role will support data coming from enterprise applications and services, including Salesforce, Hatch, NetSuite, Signal, and approved APIs. The Data Engineer will help make data available for analytics, applications, automation, and AI-enabled workflows through consistent engineering patterns,documented definitions, appropriate access controls, and dependable operational practices. Active use of AI throughout the software development lifecycle is a core expectation of this role, including AI-assisted code generation, automated testing, documentation, troubleshooting, and review with appropriate human validation.

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Data Engineer at AHEAD · web2ua

Bachelor’s degree in computer science, information systems, engineering, mathematics, or a related field, or equivalent experience.
3 or more years of experience in data engineering, software engineering, analytics engineering, or a related technical role.
Professional experience writing production-quality SQL and Python.
Experience building or supporting data pipelines, transformations, and data models in a cloud data environment.
Experience with Snowflake, dbt, or comparable cloud data warehouse and transformation technologies.
Understanding of data modeling, ELT/ETL patterns, pipeline orchestration, APIs, and source-system integration.
Experience with software engineering practices including source control, code review, automated testing, and CI/CD.
Demonstrated active use of AI-assisted software development tools for code generation, test creation, documentation, debugging, or review.
Ability to follow an AI SDLC and identify practical opportunities for multiple cooperating agents to improve delivery speed, consistency, and coverage.
Understanding of data quality, metadata, lineage, access control, privacy, and secure handling of enterprise data.
Ability to investigate data issues, communicate findings clearly, and work through ambiguity with teammates and stakeholders.
Ability to collaborate effectively with engineers, analysts, product owners, governance partners, security teams, and business stakeholders.
Preferred
Experience with Azure services, serverless functions, cloud storage, or other cloud-native data engineering capabilities.
Experience with REST or GraphQL APIs and data ingestion from enterprise applications such as Salesforce, Hatch, NetSuite, or similar systems.
Familiarity with orchestration, event-driven processing, observability, data catalogs, lineage tooling, or data quality platforms.
Experience supporting semantic models, MCP-based access, or other governed interfaces for analytics, applications, automation, or AI workflows.
Experience working with master data, reference data, entity resolution, or shared business definitions across multiple systems.
Experience operating data products with documented ownership, access expectations, quality measures, and support procedures.
Experience using AI agents or agentic workflows to support software delivery, data engineering, testing, documentation, or platform operations.
Curiosity about emerging data platform technologies and a practical approach to adopting them.

Data Engineer

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Data Engineer

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