Data Full Stack Engineer

Data Fullstack Engineer

Job Description | Broker Platform, US

Role

Data Fullstack Engineer

Platform

Broker platform (US applications)

Level

Individual contributor, 4 to 10 years of experience

Focus

Data Engineering, Data Quality, Data Analytics; Data Governance is a plus

Core stack

Snowflake, PostgreSQL, Python pipelines; ThoughtSpot for analytics

Role Summary

The Data Fullstack Engineer owns data end to end for the broker platform: ingestion, modeling, quality, and analytics delivery, with governance as an added strength. You take a business need from raw source to a trusted, self-serve insight for US brokers.

Fullstack here means the full data stack. The role covers pipelines, data quality, and analytics delivery, with the weight on data engineering. It is not an application development role.

Key Responsibilities

Data Engineering (core)

• Build and maintain Python pipelines that ingest data from internal systems, third-party feeds, and files into Snowflake and PostgreSQL.
• Design layered data models (raw, curated, serving) and optimize Snowflake performance and cost.
• Orchestrate, schedule, and monitor pipelines using Git-based version control and CI/CD.
• Use AI-assisted development to accelerate delivery, applying code review, testing, and documentation standards to all AI-generated output.

Data Quality (core)

• Define and automate validation checks for completeness, accuracy, freshness, and reconciliation against source.
• Set up alerting, root-cause analysis, and incident response for data issues.
• Maintain data contracts and SLAs with upstream and downstream teams.

Data Analytics (core)

• Build semantic models and curated datasets for broker reporting.
• Partner with business users to turn questions into metrics, definitions, and dashboards.
• Build ThoughtSpot models, worksheets, and Liveboards (nice to have).

Data Governance (nice to have)

• Contribute to cataloging, lineage, and metric definitions.
• Apply role-based access, PII handling, masking, and audit practices suited to US requirements.

AI and Innovation (nice to have)

• Support AI-powered broker platform features by preparing governed, well-modeled data for LLM and retrieval use cases.
• Apply AI to data quality, such as automated anomaly detection and schema-change handling.

Required Skills (Must Have)

• Strong SQL and Python.
• Hands-on experience with Snowflake and PostgreSQL.
• Data modeling for analytics and reporting.
• Pipeline orchestration and scheduling.
• Data quality practices: testing, monitoring, and reconciliation.
• Git and CI/CD.
• AI-assisted development: Uses tools like Claude Code or Copilot day to day for pipelines, tests, and documentation, and can review and validate AI-generated code before it ships.

Preferred Skills (Nice to Have)

• ThoughtSpot: models, worksheets, and Liveboards.
• Data governance and cataloging tools.
• AI and LLM exposure: document extraction, retrieval over curated data, AI-assisted data quality checks, and working with LLM APIs from Python.
• Real estate or broker domain knowledge.

Expectations

• Ownership: Takes work from requirement to production and supports it afterward.
• Level: Works independently at 4 years of experience and sets patterns for others toward 10. No people management.
• Collaboration: Works directly with product owners and broker-facing business teams, not only engineers.
• AI accountability: Uses AI tools as a standard part of the delivery workflow and is accountable for the quality of what ships, regardless of who or what wrote the first draft.

What Success Looks Like

• 90 days: Owns at least one pipeline in production with quality checks in place.
• 12 months: Owns a data domain end to end, with trusted metrics and measurable gains in data reliability.

Qualifications

• 4 to 10 years of experience in data engineering or a closely related field.
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent experience.