Builds performant data processing pipelines, manages relational databases, and constructs cloud-portable data lake environments using modern open storage formats and embedded analytical engines.

Pipeline Design: Construct resilient data processing pipelines in Python using Medallion architecture principles (Bronze/Silver/Gold) to transform raw data into analytics-ready assets.

Open Storage Management: Standardize data storage using open formats including Parquet, Apache Iceberg, and Delta Lake to ensure cross-cloud compatibility.

Embedded Analytics: Leverage DuckDB integrated with Python for fast, lightweight, and performant localized data processing within ETL workflows.

Database Operations: Architect and optimize production PostgreSQL schemas while utilizing SQLite for embedded or lightweight localized storage needs.

Distributed Compute: Scale heavy ETL, data transformation, and analytics workloads using Databricks or Apache Spark.

Requirements

Advanced SQL proficiency with deep PostgreSQL expertise and working knowledge of SQLite.

Hands-on experience building robust analytical pipelines using Python and DuckDB.

Mastery of open table and storage formats (Parquet, Apache Iceberg, Delta Lake).

Proven track record designing data lakes using the Medallion data processing architecture.

Familiarity with the Databricks / PySpark ecosystem for distributed processing.

About AgileEngine

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 Mid-level Data Engineer to build and maintain dbt pipelines and Snowflake data marts behind customer‑facing reporting, spend analytics, and AI features.

The mandatory requirements are solid dbt experience (modeling, tests, macros, and incremental models), ingestion and orchestration tools such as Airbyte or Prefect, 3+ years in data or analytics engineering on production pipelines, strong SQL and Python, dimensional modeling, Snowflake or a similar cloud warehouse, and an upper-intermediate English level.

What You Will Do

- Build and maintain the dbt pipelines, data marts, and warehouse models that serve in-app reporting, spend analytics, AI features, and internal analytics, treating them as production systems.
- Translate complicated procurement business logic into maintainable, well‑tested pipeline code, and notice when a model is valid‑looking but wrong.
- Build models within our data mart architecture, following the chapter's conventions for grain, conformed dimensions, and incremental patterns, and propose improvements when you find them.
- Contribute to and help buildout the semantic layer that our internal MCP servers and AI agents query,



so that metrics and dimensions are defined once and answered consistently.
- Maintain the boundary between PII and agent/reporting-safe data.
- Build and maintain the FastAPI services that expose reporting marts to customers.
- Build and maintain ingestion pipelines (Airbyte, OneSchema, S3) into the warehouse, and support our Forward Deployed Engineering function in bringing customer data into our systems.
- Implement data quality checks: freshness, schema and reconciliation tests, and alerting, so problems surface before customers find them.
- Implement metric definitions faithfully, and flag conflicting or ambiguous definitions to the chapter rather than guessing.
- Keep documentation and lineage current so that humans and AI agents can both find and trust what's in the warehouse.
- Tune Snowflake performance and cost within the models you build.
- Use agentic coding tools in your daily work, within the guardrails (tests, conventions, review patterns) the team maintains, and understand why those guardrails exist.
- Share what you learn through code review and team discussion.
- Communicate clearly with engineering, product, and customer-facing teams about what the data means and what it can and can't do.

Must Haves

- 3+ years in analytics engineering, data engineering, or a closely related role, including hands‑on work on production data pipelines that other systems or customers depended on.
- Solid dbt experience: modeling, tests, macros, and incremental models.
- Strong SQL and working Python.
- Data modeling fundamentals: dimensional modeling, data marts, grain and conformance.
- Experience with a modern cloud warehouse (Snowflake preferred).<

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