Staff Data Engineer

🕒 2 days ago

🇺🇸 United States – Remote

💵 $175k - $200k / year

⏰ Full Time

🔴 Lead

🚰 Data Engineer

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Fora Financial

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2008

🏗️ Construction

🏭 Manufacturing

🍽️ Food & Beverage

Construction • Manufacturing • Food & Beverage

Fora Financial is a business financing company that provides fast, flexible capital solutions to small and mid-sized businesses. Since 2008 it has offered products including small business loans, revenue advances, lines of credit, SBA loans, term loans, and equipment financing, with funding amounts up to $1. 5 million and expedited approval and funding timelines. Fora partners with banks and third-party funding providers to underwrite and deliver working capital across industries such as construction, manufacturing, retail, medical, and restaurants, emphasizing relationship-driven service and credit-access for businesses with varied credit profiles.

📋 Description

• Data platform architecture: ingestion patterns, warehouse design, environment strategy, orchestration, access governance, and reliability standards. • Freshness & ingestion strategy: Deciding when to use streaming versus batch based on business value, cost, and operational burden. • Cross-functional partnership: Partnering with Platform Engineering to ensure our data infrastructure integrates securely and reliably with core operational systems. • Data Integrations: requirements → source profiling → ingestion design → QA → documentation → support. • Pipeline reliability: dependencies, retries, alerts, backfills, incident response, runbooks, monitoring, and support expectations. • Legacy migration: helping retire brittle reporting paths such as Azure Data Factory, SQL backup workflows, and other duplicate pipelines. • Snowflake governance: roles, permissions, service accounts, connector ownership, environment separation, performance, cost, and governance. • Data contracts: schema-change handling, new-field availability, upstream SLAs, source defects, and escalation paths. • Data observability: freshness, volume movement, nulls, duplicates, reconciliation, anomaly detection, and critical business-rule checks. • AI-enabled leverage: using AI and automation to accelerate debugging, documentation, pipeline scaffolding, and operational workflows.

🎯 Requirements

• Deep data engineering judgment. You have designed, built, and operated production platforms, not just individual pipelines. • Hands-on depth. You move seamlessly from high-level architecture to writing production code, standing up CI/CD workflows, and debugging pipeline failures. • Strong ingestion fundamentals. APIs, CDC, backfills, idempotency, schema drift, and failure recovery. • Snowflake fluency. Warehouse design, RBAC, performance tuning, and cost controls. • Data quality discipline. You know which checks matter and make quality visible before users find issues. • Ownership & communication. You can sequence ambiguous work, write useful design docs, align technical decisions with business outcomes, and carry problems to resolution. • Cross-functional partnership. You work with stakeholders across Engineering, Analytics, and the business to understand needs, define clear requirements, and build trust. • AI leverage. You use LLMs and agents to accelerate your own work, and you build data products that agents can consume safely.

🏖️ Benefits

• Company-subsidized medical, dental, and vision plans. • 401(k) plan with company match. • Life insurance at no cost to employees. • Generous time off plan, including rollover vacation days. • Health care and dependent care flexible spending accounts. • Commuter benefits. • Remote working model. • Weekly breakfast, snacks, and Friday lunches provided onsite.