Sr 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.
• 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

• 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

Required Qualifications

• bachelors degree or higher in Computer Science, Engineering, or a related field

• 6 12 years of professional experience in Data Engineering
• Proven experience building and supporting production-grade analytical data pipelines at scale

• 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

• Strong experience with Microsoft Azure , including data lake storage and access controls
• Familiarity with lakehouse architectures and enterprise data governance concepts

Preferred Qualifications

• 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