Data Engineer IRC293383

Role Overview

Seeking Senior Data Engineer(s) with a strong Analytics Engineering background for a dedicated finance data structuring and automation project. The primary objective is to audit existing business logic, currently scattered across legacy SQL, Excel workflows, and BI reports (Metabase, Tableau), and reverse‑engineer it into a centralized, scalable layered data architecture. This role will be responsible for building well‑structured data models in Snowflake using dbt, and ultimately developing the automated pipelines, custom scripts, or reverse ETL flows necessary to export this finalized data directly into NetSuite or other enterprise ERP/accounting systems.

Required Skills

• Senior‑Level Expertise: 5+ years of experience in Data Engineering or Analytics Engineering, with a proven track record of owning end‑to‑end data modeling and integration projects.
• Cloud data warehouse: Experience with modern cloud data warehouses or lakehouse platforms (Snowflake preferable, Databricks, BigQuery or Redshift acceptable).
• Excellent SQL: Advanced to expert‑level SQL capabilities; highly proficient in complex pipeline analysis, query optimization, and cost‑efficient query design.
• Legacy & BI Reverse‑Engineering: Proven experience reverse‑engineering undocumented logic from existing BI tools, complex Excel sheets, and legacy SQL into modernized pipelines.
• Layered Architecture: Strong understanding and practical experience building Medallion (Bronze/Silver/Gold) data environments.

Preferred Skills

• Snowflake & dbt Mastery: Deep, hands‑on experience building models in Snowflake and managing transformations/testing via dbt (Core or Cloud).
• Semantic Layers: Experience building dbt or Snowflake semantic views.
• Finance Domain Knowledge: Familiarity with accounting principles, ERP data structures, and how finance teams consume data.
• ERP Integration Experience: Hands‑on experience structuring data and managing automated payloads/exports into enterprise accounting packages or ERPs (e.g., NetSuite, Workday, SAP).
• Reverse ETL: Experience with data activation / reverse ETL tools such as Hightouch or Census.
• Advanced Pipeline Orchestration: Proven experience designing fault‑tolerant, automated data pipelines using modern orchestration tooling.
• API & Integration Scripting: Proficient in Python (or similar) to build custom API connectors and automate the export of data into external systems.
• AI Implementation: Demonstrated success delivering tangible value by integrating AI into data engineering workflows. Prefer Claude, but can work with any background that can quickly translate to our AI tools.

What We’re Looking For

• Someone who is highly pragmatic and comfortable working with imperfect, real‑world data.
• Able to move quickly from ambiguity to structured solutions.
• Strong communicator who can work closely with finance, product management and non‑technical stakeholders.
• Takes ownership of both data quality and scalable platform design.
• Comfortable working in an evolving environment and helping define best practices.

Responsibilities

• Rapid Onboarding: Quickly get up to speed with existing data architecture, snapshotting, reporting logic, and business workflows.
• Reverse Engineering: Analyze and reverse‑engineer legacy reports, queries, Excel‑based processes, and existing BI dashboards (Metabase, Tableau) to understand current logic.
• Data Modeling: Design and build scalable, well‑structured data models within Snowflake using dbt.
• ERP Pipeline Automation: Develop and maintain data pipelines to transform and deliver data directly into accounting and finance systems (e.g., NetSuite).
• Schema Design: Build new schemas to support finance processes and downstream integrations.
• Must actively leverage AI methodologies and modern tooling to optimize system performance, enhance alerting infrastructure, and deliver data products that empower data‑driven decisions.
• Business Alignment: Translate business and finance requirements into clean, reusable datasets.
• Self‑Service Enablement: Enable self‑service analytics through well‑documented and reliable data models.
• Architecture Implementation: Implement and maintain a layered data architecture (e.g., bronze/silver/gold).
• Cost‑Conscious Development: Write efficient, well‑structured SQL in Snowflake with awareness of cost implications (query design, data scanning, warehouse usage).
• Performance: Optimize SQL queries and overall data performance across the platform.
• Data Activation & Export: Support data activation and operational use cases via custom API/Python scripting or reverse ETL tools such as Hightouch.