Senior Data Science Engineer
Adobe is seeking a highly skilled Senior Data Engineer to join our Doc Cloud Product & Business Analytics organization
This role focuses on building and operating large-scale analytical data pipelines and analytics-ready data tables that power product adoption, usage metrics, experimentation (A/B testing), and executive dashboards across a massive monthly active user base
This role focuses on building and operating large-scale analytical data pipelines and analytics-ready data tables that power product adoption, usage metrics, experimentation (A/B testing), and executive dashboards across a massive monthly active user base
You will work primarily on Azure Databricks, playing a key role in setting up and evolving our Databricks-based analytics data warehouse , including curated tables, metric foundations, and scalable data models
This role is ideal for a hands-on data engineer with deep expertise in SQL, Python, Spark, and Delta Lake , and a strong understanding of analytics-driven data warehousing and product analytics
Key Responsibilities This role is ideal for a hands-on data engineer with deep expertise in SQL, Python, Spark, and Delta Lake , and a strong understanding of analytics-driven data warehousing and product analytics
- Design, build, and maintain large-scale, production-grade data pipelines on Azure Databricks using Apache Spark and Python
- Write and optimize complex, high-performance SQL for data transformation, aggregation, and analytics workloads at scale
- Develop and maintain analytics-ready data models (fact tables, dimensions, rollups, metric layers, and gold layer tables) used for dashboards and reporting
- Optimize Databricks workloads for performance, reliability, and cost efficiency , including tuning Spark jobs and Delta Lake tables
- Establish and apply Delta Lake best practices , including incremental and idempotent processing, MERGE patterns, partitioning, and table optimization
- Partner closely with product analysts, business analysts, data scientists, and product managers to enable reliable, self-serve analytics, supporting product-led growth use cases
- Implement data quality checks, validation frameworks, and monitoring to ensure accurate and trusted analytics metrics
- Apply strong Data engineering best practices , including version control and documentation
- Contribute to solutions that integrate structured and unstructured data , including selective use of GenAI / LLM-based capabilities where relevant
Education
- bachelors degree or higher in Computer Science, Engineering, or a related field
Experience
- 6 12 years of professional experience in Data Engineering
- Proven experience building and supporting production-grade analytical data pipelines at scale
Technical Expertise (Must-Have)
- Expert-level SQL skills, including complex joins, window functions, performance tuning, and large-scale aggregations
- Advanced Python proficiency for data processing, pipeline development, and automation
- Deep hands-on experience with Azure Databricks and Apache Spark
- Strong understanding of Delta Lake and optimization techniques (partitioning, Z-ordering, compaction)
- Experience designing data models optimized for analytics and BI consumption
Cloud & Platform
- Strong experience with Microsoft Azure , including data lake storage and access controls
- Familiarity with lakehouse architectures and enterprise data governance concepts
- Experience with streaming or near real-time data pipelines on Databricks
- Prior experience supporting product analytics, feature adoption, or MAU-based metrics
- Exposure to MLOps, LLM deployment, or GenAI-enabled data applications
- Familiarity with BI tools such as Tableau or Power BI and their performance considerations
- Experience mentoring analysts or junior data engineers
