Finance Staff Data Engineer, AI Native
New
Within the US, CanadaFull-TimeStaff
Salary190000 - 280500 USD per year
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Job Details
- Experience
- 8+ years
- Required Skills
- AWSPythonSQLAirflowSparkCI/CDTerraformdbtDatabricksGitHub ActionsLLM
Requirements
- 8+ years designing and operating high-volume distributed data systems in production.
- Deep expertise with a cloud data platform (Databricks strongly preferred) and AWS from an infrastructure / services architecture, deployment, and ownership perspective.
- Strong proficiency in Python, SQL, and Spark for large-scale processing.
- Strong proficiency with modern CI/CD practices (creating GitHub Actions, writing Terraform code to manage infrastructure in Databricks / Airflow / AWS / and others).
- Hands-on experience with dbt from an infrastructure / deployment perspective and understanding of how platform decisions impact downstream modeling.
- Strong grasp of data modeling, partitioning strategies, storage formats, and analytical workload optimization.
- Experience with Airflow and data flow orchestration.
- Experience with networking challenges in data ingestion (e.g., VPC peering, firewall traversal, API rate limiting, cross-AWS account access, etc.).
- Able to effectively leverage / oversee LLM-supported code development while maintaining a high quality bar.
- Demonstrated experience with AI tools to support / enhance development - Claude Code, Cursor, etc.
- Demonstrated ability to independently scope ambiguous problems and drive them to decisive outcomes.
- Track record of proactively escalating risks and closing long-running efforts with clear recommendations.
- Experience defining ingestion validation standards and implementing data quality controls.
- Proven ability to reduce operational fragility and eliminate single points of failure.
- Strong systems design skills across distributed and event-based architectures.
- Demonstrated technical leadership influencing cross-team architectural decisions.
- Excellent communication skills across engineering, analytics, product, and executive stakeholders.
- BS in Computer Science, Engineering, Mathematics, or equivalent experience.
Responsibilities
- Architect and evolve scalable data ingestion and egress frameworks and pipelines that are well tested and offer strong data quality monitoring.
- Architect and evolve our CI/CD processes - enhancing the testing environment and observability.
- Architect delivery architecture of data assets to external team partners to reduce manual operational overhead associated with month end close.
- Enhance our Claude Code / LLM development support capabilities - creating tools / skills / agents that give our LLMs more context.
- Enhance our security posture in our AWS / Databricks environment.
- Design and implement distributed data processing systems using Spark and Databricks on AWS.
- Establish clear ingestion and integration boundaries that eliminate single points of failure.
- Proactively surface risks, dependencies, and tradeoffs before they impact delivery.
- Produce clear technical artifacts and recommendations for stakeholders and leadership.
- Design logical and physical data models balancing flexibility, performance, governance, and scalability.
- Partner closely with the Analytics Engineers on the Finance Data Team to support high-quality downstream data modeling & reporting.
- Harden pipelines with monitoring, alerting, SLAs, and recovery mechanisms.
- Mentor engineers and elevate distributed systems rigor across the team.
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