We are looking for a Senior Data Engineer to design and build scalable, high-quality data products that power analytics, reporting, and AI use cases. You'll play a key role in defining event modeling standards, building a trusted metrics layer, and developing modern transformation workflows using dbt. You'll also contribute to emerging GenAI initiatives, leveraging Python and a foundational understanding of LLMs (Large Language Models).

Key Responsibilities :

Data Modeling & Analytics Enablement :

- 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.

dbt & Transformation Development :

- Build and maintain transformation pipelines using dbt, including :

1. modular models, sources, and documentation

2. data tests (generic + custom)

3. incremental models and performance tuning

- Establish best practices around branching, deployment, and CI/CD for dbt projects.

Data Platform & Quality :

- 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.

GenAI & LLM Support :

- 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.

Python Development :

- Build utilities, automation scripts, and data workflows using Python.

- Use Python for validation frameworks, pipeline tooling, and integration across systems.

Required Qualifications :

- 4+ 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.

Preferred Qualifications (Nice to Have) :

- 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.).