Autodesk
Principal Data Engineer, Growth Analytics
Vaga remota de Data Engineering com fit claro de localização do candidato.
Publicada14 de jun. de 2026
Países elegíveis1 país aceito
Sinal de senioridadeSenior
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Estados Unidos
Resumo da vaga
Principal Data Engineer, Growth Analytics
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
Responsibilities
- Architect and build the core Growth Analytics data environment with Snowflake as the central platform
- Design, implement, and maintain scalable data pipelines, integrations, and transformation workflows across internal business, product, operational, sales, marketing, finance, and planning systems
- Build trusted, reusable data models and curated datasets to support internal analytics, reporting, forecasting, growth planning, and Strategy & Operations workflows
- Develop and manage BI environments in Power BI and Looker, including semantic models, governed datasets, dashboard foundations, access patterns, and performance standards
- Build AI-ready data layers to support internal intelligence solutions, including structured datasets, metadata, documentation, business definitions, and governed access patterns
- Establish standards for data quality, testing, observability, lineage, documentation, naming conventions, metric definitions, and production reliability
- Partner with internal stakeholders to translate business questions into scalable data products, reporting layers, and analytics-ready infrastructure
- Create and maintain clear documentation for source systems, pipelines, data models, metric definitions, BI assets, and AI-ready datasets
- Lead technical design discussions, make architecture recommendations, and mentor analytics, BI, and data partners on scalable data practices
- Help move AOS from fragmented reporting and manual data processes toward a reliable, governed, and scalable intelligence layer
Minimum Qualifications
- 8+ years of experience in data engineering, analytics engineering, data platform engineering, or a related technical role
- Proven experience building data platforms, analytics environments, or major data infrastructure from scratch or through significant transformation
- Deep hands-on experience with Snowflake
- Strong Scripting, SQL and Python skills
- Experience building production-grade pipelines, integrations, orchestration workflows, and transformation logic
- Experience with modern data ingestion tools ingestion tool such as Fivetran, Airflow + Python, dbt, Pyspark or similar technologies
- Experience building BI environments such as Power BI or Looker
- Strong understanding of data modeling, semantic layers, governed metrics, access control, documentation, lineage, data quality, and observability
- Experience preparing data infrastructure for AI, ML, LLM, advanced analytics, or internal intelligence use cases
- Ability to work with ambiguity, clarify requirements, and turn business needs into scalable technical solutions
- Strong communication skills with technical and non-technical stakeholders
- Experience supporting internal analytics, growth analytics, product Strategy & Operations, GTM analytics, finance analytics, or business operations teams
- Experience integrating data from systems such as Salesforce, Marketo, finance platforms, product usage systems, planning tools, and operational systems
- Experience creating AI-ready data assets, metadata layers, governed knowledge layers, feature-ready datasets, or semantic models
- Experience designing standards for BI development, data documentation, metric governance, and data product delivery
- Experience in B2B SaaS, enterprise software, or complex matrixed organizations
- Track record of building zero-to-one data infrastructure in an environment with fragmented systems, evolving requirements, and limited existing standards
Success in This Role Looks Like
- A reliable Snowflake-based data foundation is in place for AOS Analytics
- Key internal data sources are connected through scalable, observable pipelines
- Power BI and Looker environments are governed, performant, and easier for teams to use
- Core metrics and business definitions are documented and consistently applied
- Internal stakeholders and product Strategy & Operations teams can access trusted data products instead of relying on manual reporting
- AI-ready datasets, metadata, and documentation are available to support internal intelligence and advanced analytics solutions
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