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# Analytics Engineer

- **Company:** Confido, LLC
- **Location:** New York, NY, United States
- **Salary:** $150,000.0 - $190,000.0
- **Contract:** Permanent contract
- **Skills:** Adaptable Database Systems, Business Analytics Applications, Data Analysis, Big Data, Data Architecture, Data Infrastructure, Extract Transform Load (ETL), Data Security, Data Warehousing, Database Queries, Distributed Computing Environment, Snowflake, Data Pipelines
- **Published:** June 24, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0660928a88cf9b2a

## About the Role

Do you have experience in Tooling?, * Experience building and operating modern data warehouses and analytics platforms.

 * Strong SQL skills and experience modeling large datasets.
 * Deep experience with distributed data processing, including designing performant ETL pipelines and optimizing large-scale data workloads.
 * Experience with data pipeline orchestration, ETL/ELT workflows, and data infrastructure.
 * Familiarity with modern analytics tooling such as Snowflake, dbt, or similar technologies.
 * Ability to balance immediate business needs with long-term platform investments.
 * Excellent communication skills and a track record of working cross-functionally with technical and non-technical stakeholders.

## Description

At Confido, data powers everything from customer-facing insights to internal decision making. We are looking for an Analytics / Data Engineer to build the foundation that enables scalable reporting, trusted metrics, and seamless data access across the company.

What You'll Do

 * Design, build, and maintain our centralized data warehouse and analytics infrastructure.
 * Develop scalable data models that power reporting, dashboards, operational workflows, and customer-facing data exports.
 * Serve as the steward of Confido's data landscape, creating visibility into data ownership, lineage, and dependencies while ensuring data is discoverable, trustworthy, and easy to consume across the organization.
 * Consolidate and standardize data models across multiple products and teams, creating a single source of truth for the business.
 * Establish data engineering best practices, including data modeling standards, testing, documentation, monitoring, and governance.
 * Partner closely with Engineering, Product, Operations, and Customer teams to define metrics and deliver actionable insights.
 * Improve data quality, reliability, and accessibility across the organization.
 * Enable self-service analytics by creating scalable datasets and semantic layers for downstream consumers.


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