Best suited to candidates with experience in cloud data engineering, particularly with Snowflake and dbt, working in a collaborative, product-oriented environment in the United States.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Thinkahead via Lever
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.
Working under the Director, Data Platform and alongside the Data Governance Lead, this role will contribute to a product-oriented engineering team. The role will partner with data consumers and other engineering teams to understand requirements, deliver useful platform capabilities, and improve the speed and consistency of data delivery.
Duties/Responsibilities:
- Build, maintain, and improve batch and low-latency data ingestion pipelines from enterprise systems, APIs, and other approved sources.
- Follow the AI SDLC by actively using approved AI coding tools and agents to generate, refactor, explain, and review code; validate generated output through engineering judgment, testing, and peer review.
- Use AI to generate and improve unit, integration, data-quality, and regression tests, then verify that automated tests accurately validate the intended behavior.
- Use AI-assisted workflows to create and maintain technical documentation, data-product documentation, runbooks, lineage notes, and change summaries as part of delivery.
- Build toward coordinated multi-agent delivery patterns that can divide and accelerate discovery, implementation, testing, documentation, and operational support while preserving human accountability.
- Develop SQL and Python solutions that collect, validate, transform, and publish data for downstream consumption.
- Use Snowflake and dbt to implement reliable transformations, reusable models, curated datasets, and data products across raw, common, and curated layers.
- Translate business and technical requirements into source mappings, data models, acceptance criteria, and maintainable engineering solutions.
- Partner with analytics, application, AI, Integration Platform, and business teams to make data available through governed and documented access patterns.
- Apply data quality checks for completeness, freshness, uniqueness, consistency, referential integrity, and other relevant quality dimensions.
- Add metadata, documentation, lineage, ownership, and usage guidance to data products so consumers can find and understand the data they use.
- Implement secure access patterns in partnership with Data Governance and Security teams, including role-based access, classification tags, masking, and row- or column-level controls when appropriate.
- Build automated tests and deployment processes that support consistent delivery through development, quality assurance, and production environments.
- Monitor pipeline health, data freshness, processing performance, and failures; troubleshoot issues and participate in incident resolution.
- Optimize Snowflake workloads, queries, transformations, and storage patterns for performance, reliability, and cost discipline.
- Support the curation and publication of cross-system data needed for shared business context, entity-aware access, reporting, automation, and AI use cases.
- Work with the Integration Platform and semantic-layer capabilities, including Horizon, to support consistent business meaning and reusable data access.
- Participate in backlog refinement, estimation, code review, technical documentation, and iterative delivery within an Agile engineering team.
- Identify opportunities to simplify delivery, reduce duplicate work, improve platform standards, and strengthen the reliability of data engineering practices.
Education and Experience:
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.
Physical Requirements:
- Ability to safely and successfully perform the essential job functions consistent with the ADA, FMLA, and other federal, state, and local standards, including meeting qualitative and/or quantitative productivity standards.
Ability to maintain regular, punctual attendance consistent with the ADA, FMLA, and other federal, state, and local standards.
Primarily office and computer-based work with standard engineering and collaboration expectations for an enterprise technology role.
Source: Thinkahead careers (Lever)