Senior/Lead Data Engineer

VWO is seeking a Senior/Lead Data Engineer to design and build scalable data products that enhance analytics and reporting. The role involves defining event modeling standards, developing transformation workflows, and contributing to GenAI initiatives.

Responsibilities:

• Strong experience building Analytics Data Warehouses (DWH) using dimensional modeling, including SCD (Slowly Changing Dimensions Type 1/2), incremental loading strategies, and star/snowflake schema design
• Design and implement scalable event data models that support product analytics and behavioral insights
• Develop and maintain a governed metrics layer (definitions, calculation logic, validation, and documentation)
• Build and optimize a semantic layer that enables consistent reporting across BI tools and downstream consumers
• Partner with Sales, Marketing, Support, Product, and Engineering teams to define reliable, reusable datasets and business logic
• Build and maintain transformation pipelines using dbt, including: modular models, sources, and documentation; data tests (generic + custom); incremental models and performance tuning
• Establish best practices around branching, deployment, and CI/CD for dbt projects
• Ensure high data quality through proactive testing, observability, and monitoring
• Improve dataset reliability and maintainability through naming conventions, contracts, and lineage management
• Troubleshoot pipeline issues and resolve data inconsistencies quickly and effectively
• Support integration of data with LLM-based applications (e.g., data narrator, metadata generation, dataset summarization, etc.)
• Apply a basic understanding of LLM concepts such as embeddings, prompts, vector search, and token limits to guide data design
• Build utilities, automation scripts, and data workflows using Python
• Use Python for validation frameworks, pipeline tooling, and integration across systems

Requirements:

• 6+ years of experience in Data Engineering or similar roles
• Strong experience in data warehousing
• Strong experience with event modeling (product events, behavioral data)
• Proven ability to build and manage a metrics layer and semantic layer for consistent analytics
• Hands-on expertise with dbt for building production-grade transformation models
• Strong Python skills for data engineering workflows and automation
• Familiarity with GenAI concepts and modern AI/data workflows
• Basic understanding of LLMs, including how data is used in LLM applications
• Strong SQL skills and experience working with modern data warehouses (Snowflake/BigQuery/Redshift or similar)
• Excellent communication skills and ability to collaborate with cross-functional stakeholders
• Experience building a semantic layer tool (e.g., dbt Semantic Layer, Cube, MetricFlow, etc.)
• Experience with data orchestration tools (Airflow, Dagster, Prefect)
• Familiarity with data observability tools (OpenMetaData, Monte Carlo, Datadog, etc.)
• Experience supporting ML features, embeddings pipelines, or vector databases
• Experience working in product analytics ecosystems (Segment, Mixpanel, etc.)