Fora Financial

Staff Data Engineer

Remote Data Engineering role with clear candidate location fit.

PostedJul 2, 2026

Eligible countries6 accepted countries

Seniority signalSenior

Work settingRemote

Accepted candidate locations

AustraliaCanadaGermanyIndiaUnited KingdomUSA

Role overview

Readable role content extracted into sections for faster review.

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.

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.

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.