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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.