Staff Data Engineer

About this role

Staff Data Engineer

About the role

Fora Financial is at an inflection point, modernizing our legacy stack to build the foundation for AI-native analytics. To lead this effort, we are hiring a Staff Data Engineer to build and own the platform backbone for governed reporting and trusted AI workflows.

This is a hands-on Staff IC role on a small Data & AI team. You will make strategic architecture calls—from ingestion patterns and Snowflake design to data contracts and SLAs—and then get into the weeds to build, harden, or rebuild pipelines.

We are looking for a systems thinker who understands business impact and operational burden, and who can partner closely with Analytics, Engineering, and vendors to turn fragmented source systems into trustworthy data products.

What you will own

Architecture & Strategy

• Data platform architecture: ingestion patterns, warehouse design, environment strategy, orchestration, access governance, and reliability standards.

• Freshness strategy: deciding which data needs real-time, near-real-time, daily, or ad hoc refreshes — and designing accordingly.

• Streaming vs. batch decisions: making pragmatic tradeoffs across business value, cost, complexity, failure modes, and operational burden.

Execution & Reliability

• Source ingestion: batch, incremental, API-based, file-based, CDC, and streaming patterns where they make sense.

• Pipeline reliability: dependencies, retries, alerts, backfills, incident response, runbooks, monitoring, and support expectations.

• New source onboarding: requirements → source profiling → ingestion design → QA → documentation → support ownership.

• Legacy migration: helping retire brittle reporting paths such as Azure Data Factory, SQL backup workflows, TRS Daily, and other duplicate pipelines.

Governance & Quality

• Snowflake platform operations: 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 quality and observability: freshness, volume movement, nulls, duplicates, reconciliation, anomaly detection, and critical business-rule checks.

• AI-enabled leverage: using AI and automation to improve debugging, documentation, pipeline scaffolding, testing, monitoring, and operational workflows.

What we are looking for

• Deep data engineering judgment. You have designed, built, and operated production platforms, not just individual pipelines.

• Hands-on depth. You seamlessly move from architecture discussions to Python, SQL, deployment scripts, and production debugging.

• 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.

• Independent ownership & communication. You can sequence ambiguous work, write useful design docs, align technical decisions with business outcomes, and carry problems to resolution.

• AI-native leverage. You actively use LLMs and agents to accelerate engineering work without outsourcing judgment.

Nice to have

• Lending, fintech, or financial-services data experience.

• CDC, Debezium, Fivetran, Airbyte, Azure Data Factory, dbt Cloud, Dagster, Airflow, Prefect, or equivalent tooling.

• Snowflake performance tuning, RBAC, data sharing, warehouse cost optimization, or Iceberg.

• Data observability with Monte Carlo, Elementary, dbt tests, custom monitors, or similar.

• Data contracts, source SLAs, or schema-change processes with Engineering teams.

• AI-native analytics, semantic layers, MCP servers, agent QA, or governed context retrieval.

• Lightweight internal tools, scripts, or agents that reduce repetitive platform work.

Compensation and logistics

• Base salary: $175,000–$200,000

• Fully remote within the US; Eastern or Central time zones preferred.

• Reports to the VP of Data & AI.

• Final compensation is based on scope of past ownership, technical judgment, and ability to set direction independently.

Benefits of working at Fora Financial

• 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.

About Fora Financial

Fora Financial is a technology-enabled provider of flexible financing to small and medium-sized businesses. Since 2008, we have supported more than 55,000 merchants nationwide with over $5 billion in capital for operating expenses, cash-flow management, and growth.

Our proprietary technology helps deliver capital through a streamlined process that can be completed in as little as 24 hours, compared with the weeks or months often required for a traditional bank loan. Fora has grown from two founders in a small Manhattan workspace to a company of nearly 200 employees with a collaborative, partner-centric culture.

Equal Opportunity Statement

Fora Financial is an Equal Opportunity Employer. We are committed to a diverse and inclusive workplace where all individuals are treated with respect and dignity. We do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, disability, age, veteran status, or any other legally protected status under local, state, or federal law. Fora Financial provides reasonable accommodations for qualified individuals with disabilities.

Similar jobs

• Staff Data Engineer — Oxio · Remote
• Staff Data Engineer — Onxmaps · Bozeman, Montana, United States
• Staff Data Engineer — Crunchyroll · Los Angeles, California, United States
• Staff Data Engineer — Clickup · Remote
• Staff Data Engineer — Copperhome · Berkeley, California, United States
• Staff Data Engineer — Checkr · Denver, Colorado, United States; San Francisco, California, United States
• Staff Data Engineer — Artisan · San Francisco
• Staff Data Engineer — Arine · Remote