Note: The job is a remote job and is open to candidates in USA. Fora Financial is a technology-enabled provider of flexible financing to small and medium-sized businesses. They are seeking a Staff Data Engineer to lead the modernization of their data stack, focusing on building and owning the platform backbone for governed reporting and trusted AI workflows.
Responsibilities
- 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
Skills
- 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
- Lending, fintech, or financial-services data experience
- CDC, Debezium, dbt Cloud, Dagster, Airflow, or equivalent tooling
- Fluency with Azure data services such as Event Hubs, Blob Storage, Azure SQL, and Azure DevOps
- 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, agentic orchestration, or governed context retrieval
- Familiarity with open table formats such as Apache Iceberg
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.
Company Overview
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