Staff Data Engineer

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

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
• 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