DWH Architect (Data Modeling)

About the job

• Set up the data modeling practice, processes and platform. Own the data models that make up our data warehouse.
• Lead the highly iterative dimensional modeling process.

Responsibilities

• Develop a comprehensive understanding of our products and operations and become intimately familiar with the source data and its generating systems, using a combination of documentation, direct interaction with engineering teams, and systematic data profiling.
• Collaborate with the data, product, and business teams to gather and distill their reporting and analysis requirements and work together to design effective data models that accurately represent the underlying business processes.
• Establish data modeling and naming standards that promote consistency and best practices across the data warehouse and communicate these standards effectively to the broader data team.
• Define and maintain a high-level data warehouse architecture, including fact, dimension, and aggregation layers, and the relationships between them.
• Work with members of the data team to design data models that effectively measure performance and capture the performance drivers for the various business processes in our complex business.
• Write, maintain, and communicate data design documentation to ensure that the broader data team has a clear understanding of our data models and how they work.
• Oversee the implementation of data models, ensuring that they are accurate, efficient, and scalable, and that they meet the reporting and analysis needs of the broader organization.
• Develop, maintain, and manage the end-to-end data modeling process, from requirements gathering to model design to implementation and ongoing maintenance.
• Collaborate with the data engineering team to design effective data pipeline infrastructure, abstractions, and tooling that support our data modeling efforts.
• Collaborate with cross-functional teams to understand specific analytics requirements and create standardized data models that empower end-users to independently generate insights through self-service analytics tools.
• Advocate for data modeling depth, rigor, and consistency across the data and business teams, and coach data scientists and engineers on the principles of effective data modeling.

Requirements

• A Bachelor's or Master's degree in Engineering, Computer Science, Technology, or similar is required.
• 5+ years of experience working in data, with experience spanning multiple data disciplines, including data science, data engineering, data analysis, and data warehousing.
• 3+ years of experience in data modeling at a high-growth consumer product company or similar.
• Keen on gaining a deep understanding of our business, product, and operations. You should have a piercing eye for important business questions that the data models would be able to answer.
• A drive for consistency, a mind for abstraction, and attention to detail are necessary traits for this role.
• Experience with modeling diverse product and business processes, including operational process data, D2C, Growth data, Customer experience, Strategic data, scrapping data, Subscription data, user experience, experimentation, and financial data.
• A strong grasp of dimensional modeling concepts, such as fact table grain definition, logical views, dealing with non-additive facts, periodic and accumulating snapshots, changing dimensions, and bridge tables.
• Experience in designing data pipelines, including identifying dependencies, opportunities for abstraction, and performance bottlenecks.
• Deep familiarity with relevant technologies is a plus.

Technologies We Use

• Data Warehouse / Big Data: Snowflake, ADL, and Postgres
• Reporting: Power BI
• Data Pipelines: Kafka, Python, SQL, Airflow, Airbyte, and DBT
• ML Ops: Amazon SageMaker
• Platform: AWS
• Repository: Gitlab
• Analysis: SQL, Python, and Excel
• Presentation: Power BI, Excel 365, and PowerPoint
• Documentation: Confluence

Hope this helps! Let me know if you need anything else.