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Overview

The client is one of the fastest-growing NYC‑based tech companies that is on a unique mission to empower America’s workforce to take control of their financial lives by disrupting archaic payroll processes and their consequences.

Through rich and seamless partnerships with our clients, we have become the pioneers in providing employees real‑time access to earned wages daily – across all industries – so employees can pay bills on time and avoid late fees and payday loans.

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.

Requirements

Required Skills (Must Haves)

- 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.

- Experience with modern cloud data warehouses or lakehouse platforms – Snowflake preferable, Databricks, BigQuery or Redshift acceptable.

- Advanced to expert‑level SQL capabilities; highly proficient in complex pipeline analysis, query optimization, and cost‑efficient query design.

- Proven experience reverse‑engineering undocumented logic from existing BI tools, complex Excel sheets, and legacy SQL into modernized pipelines.





- Strong understanding and practical experience building Medallion (Bronze/Silver/Gold) data environments.

Preferred Skills (Nice to Haves)

- Deep, hands‑on experience building models in Snowflake and managing transformations/testing via dbt (Core or Cloud).

- Experience building dbt or Snowflake semantic views.

- Familiarity with accounting principles, ERP data structures, and how finance teams consume data.

- Hands‑on experience structuring data and managing automated payloads/exports into enterprise accounting packages or ERPs (e.g., NetSuite, Workday, SAP).

- Experience with data activation / reverse ETL tools such as Hightouch or Census.

- Proven experience designing fault‑tolerant, automated data pipelines using modern orchestration tooling.

- Proficient in Python (or similar) to build custom API connectors and automate the export of data into external systems.

- Demonstrated success delivering tangible value by integrating AI into data engineering workflows.

What We’re Looking For

- 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.

Job 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.

- 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.

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Required Skill Profession

Other General

Postúlate en Kit Empleo: kitempleo.com.ar/empleo/qah0u

📌 Data Engineer3 (Buenos Aires)
🏢 Globallogic
📍 Buenos Aires