Senior Data Engineer ID89366

Job Description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for a Senior/Lead Data Engineer to build and maintain the pipelines and data models behind a centralized HR data warehouse. Working in Snowflake, DBT, Python, and Airflow, this person designs semantic layers that make HR data consumable by both BI tools and AI agents. Experience with Snowflake Cortex or MCP-based tooling for agentic querying is a strong plus.

WHAT YOU WILL DO

- Develop and maintain secure, efficient data pipelines using DBT, PySpark, and Python applications within a supported cloud environment for HR data sources.

- Build the infrastructure and tooling required for optimal extraction, transformation, and loading of data from a wide variety of sources using Python, DBT, Terraform, and AWS technologies such as Glue, EMR, and S3.

- Identify and implement internal process improvements, develop tests within the Data Quality Framework, monitor pipelines to maintain 99.5% uptime, and create and maintain the data models used by partner teams.

- Design and build semantic views and ontology layers on top of existing dbt models (CORE → SEMANTIC → METRICS) so HR data is consumable by both BI tools and AI agents/LLMs, defining business-friendly entities, relationships, and metrics that abstract away raw warehouse structure.

- Develop and deploy AI agents (e.g., via Snowflake Cortex Analyst/Cortex Search) for HR COE use cases — including PE, iCIMS, LMS, and Sales/CS Worker agents — enabling natural-language querying over certified HRDM data.

- Configure and maintain MCP (Model Context Protocol) connections between Snowflake and internal AI tooling to securely expose HR data to conversational and agentic interfaces.

MUST HAVES

- 4+ years of experience.

- Snowflake, including data modeling, datamarts, and data warehouse design.

- DBT for data transformations.

- Python, including object-oriented programming and data scripting.

- Airflow for pipeline orchestration.

- REST API integration and data ingestion.

- Google BigQuery querying and optimization.

- Secure handling of large-scale and sensitive data.

- Real-time data processing (e.g., from Google Sheets or APIs).

- Strong SQL, including highly optimized queries.

- Comprehensive documentation skills.

- Upper-intermediate English level.

NICE TO HAVES

- Semantic layer / semantic modeling design, providing business-object abstraction over dbt and warehouse models.

- Snowflake Cortex (Cortex Analyst, Cortex Search) or equivalent LLM-native query layers.

- MCP (Model Context Protocol) or similar tool-calling / context-exposure frameworks.

- Familiarity with prompt and context engineering for grounding LLM agents in certified data sources.

PERKS AND BENEFITS

- Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.

- Competitive compensation: We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.

- A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.

- Flextime: Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.

Requirements
+4 years of experience. Snowflake, including data modeling, datamarts, and data warehouse design. DBT for data transformations. Python, including object-oriented programming and data scripting. Airflow for pipeline orchestration. REST API integration and data ingestion. Google BigQuery querying and optimization. Secure handling of large-scale and sensitive data. Real-time data processing (e.g., from Google Sheets or APIs). Strong SQL, including highly optimized queries. Comprehensive documentation skills.