Senior Data Engineer

Adobe•San Jose, CA

2d•$139,000 - $257,550

About The Position

We are looking for a Senior Data Engineer to join our Data & Analytics Engineering team. In this role, you will develop and maintain scalable data pipelines that support product usage reporting and enterprise analytics across our products. You will operate where data architecture, data quality, and multi-functional ownership meet to ensure data is trusted, validated, and reliably delivered from product instrumentation to executive dashboards.

Requirements

5+ years of experience in data engineering, data architecture, or a closely related field.
Proficiency in unified data platforms like Databricks, Snowflake.
Proficiency in SQL and at least one modern data transformation framework (e.g., dbt, Spark, Dataflow).
Hands-on experience building and maintaining high-capacity data integration solutions in cloud environments (AWS, Azure, or GCP).
Experience with semantic modeling and BI consumption layers, including familiarity with tools such as Power BI, Tableau, or Looker.
Demonstrated ability to define and detail data ownership models, RACI frameworks, or data governance policies.
Strong communication skills — able to clearly explain data concepts and architectural decisions to technical and non-technical audiences.

Nice To Haves

Experience with product usage / event data (e.g., Amplitude, Segment, Adobe Analytics, or custom instrumentation).
Familiarity with data contracts, schema registries, or data mesh principles.
Experience with Power BI dataflow, datasets, or enterprise-scale report delivery.

Responsibilities

Build and implement end-to-end data pipelines spanning product instrumentation, ingestion, transformation, semantic modeling, and reporting layers (e.g., Power BI).
Develop and maintain documentation for source-of-truth datasets, including ownership of metric definitions, data joins, schema transformations, and lineage tracking.
Lead data modeling efforts, including entity relationship build, dimensional modeling, and consumption-layer optimization.
Establish and maintain clear ownership models across the data lifecycle — from ingestion to semantic model to reporting.
Define RACI frameworks for data accountability, clarifying which roles are responsible, accountable, consulted, and informed at each stage.
Ensure downstream teams are proactively notified and protected when upstream schema or logic changes occur.
Build and enforce data validation strategies at the pipeline level and at semantic/reporting consumption layers.
Define and implement quality gates that must pass before dashboards or metrics are considered production-ready.
Build monitoring, alerting, and anomaly detection systems to proactively surface data quality issues.
Conduct root-cause analysis on data incidents and drive resolution across teams.
Act as a technical point of contact between Data Engineering, Product, Analytics, and BI teams.
Mentor junior data engineers and contribute to team standards and ongoing skill development.
Participate in architectural reviews and sign-off processes for new data products and features.