Epicor Kinetic consultant leading master, transaction, and historical data migrations. Supporting a global industrial equipment manufacturer’s post - merger platform standardization.
About the role
- Principal Data Engineer architecting Snowflake, dbt, and streaming infrastructure for JobGet’s AI-powered hiring platform. Enabling real-time matching, analytics, and machine-learning data products.
Responsibilities
- Lead technical direction for how data flows, scales, and powers decisions at JobGet
- Own the data architecture behind the platform connecting job seekers with employers
- Lead design reviews for critical platform components and evaluate scalability, cost, reliability, and time-to-insight trade-offs
- Evaluate and introduce modern data stack tooling such as Databricks, Snowflake, dbt, and Kafka
- Own observability, alerting, and operational readiness standards for tier-1 pipelines
- Design and implement robust batch data pipelines using Snowflake and dbt
- Establish data modeling standards, including schema design, dimensional modeling, and data contracts
- Solve complex data integration, performance, and reliability challenges through hands-on implementation
- Architect and evolve streaming infrastructure using KSQL and Apache Flink
- Partner with Platform Engineers and Data Analysts to design feature stores, training data pipelines, and inference-ready datasets
- Ensure data products are well-modeled, documented, and performant for self-serve analytics
- Evaluate and integrate emerging AI-powered tools
- Define and enforce data governance standards for quality, lineage, access control, and cataloging
- Implement data validation frameworks, SLA monitoring, and anomaly detection
- Design privacy-by-default data systems aligned with CCPA, GDPR, and other applicable regulations
- Mentor and level up data engineers through code reviews and design guidance
- Collaborate with Software Engineering, Product, and Analytics on data requirements
- Drive adoption of data platform standards and architectural patterns across teams
- Identify and fix bottlenecks, architectural bugs, and performance issues affecting system stability
Requirements
- 10+ years of data engineering experience
- At least 3–5 years operating at a principal or staff IC level in a start-up environment
- Production-grade experience with Snowflake and dbt (required)
- Experience with real-time streaming technologies such as KSQL or Apache Flink strongly preferred
- Hands-on AWS experience at scale, including cloud data architecture, cost governance, and infrastructure-as-code
- Terraform experience is a nice-to-have
- DataOps experience, including CI/CD for data pipelines, automated testing frameworks, and deployment practices
- Significant, demonstrated AI usage in day-to-day engineering work required
- Expert-level proficiency in Python and SQL
- Strong command of distributed systems, data pipeline design, and performance optimization
- Strong data modeling background, including dimensional modeling, schema design, and data contracts
- Experience building data infrastructure supporting ML/AI systems, including feature pipelines, training data management, and inference-layer data serving
- Demonstrated track record of setting technical standards across teams through RFCs, architecture reviews, or platform migrations
- Strong grasp of data governance fundamentals, including data lineage, cataloging, access control, and quality monitoring
- Familiarity with real-time and near-real-time data architectures, including streaming, event-driven design, and CDC
- Exceptional communication and stakeholder management skills
- Builder’s mentality and operator’s discipline
Benefits
- Purpose-driven organization
- Flexible time off
- Remote-first
- Flexible work hours
- Medical & dental plans
- Parental leave
- Employee stock options
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
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