Senior Data Engineer (GCP • Python • Iceberg • Delta Lake • Kafka • Snowflake • Databricks)
Summary
Senior Data Engineer builds GCP-based lakehouse pipelines in Python/Spark, enabling cross-platform data sharing between BigQuery, Snowflake, and Databricks using Iceberg UniForm, Delta Sharing, and Kafka CDC.
Railroad19, Inc is seeking a Senior Data Engineer with deep, hands‑on experience in building modern lakehouse architectures on GCP. This role focuses on designing, developing in Python & Spark, and delivering reusable data‑sharing adapters that connect BigQuery‑backed data products to Snowflake and Databricks using Iceberg UniForm and Delta Sharing.
About Railroad19:
At Railroad19, Inc, we develop customized software solutions and provide software development services. We’re a specialized team of developers and architects. As such, we only bring an “A” team to the table, through hard work and a desire to lead the industry — this is our company culture — this is what sets Railroad19 apart.
As a Railroad19 employee, you will be part of a company that values your work and gives you the tools you need to succeed. Our headquarters is in Saratoga Springs, New York, but this position is 100% remote. Railroad19 provides competitive compensation and excellent benefits, including Medical/Dental/Vision/Pet Insurance, Paid Time Off, and 401 (k).
NO 1099, C2C, Corp-to-Corp; only full-time employment.
NO Agencies.
Core Responsibilities:
- Design and implement the UniForm write layer (Delta + Iceberg dual metadata).
- Build GCS → BigQuery ingestion pipelines for structured operational datasets.
- Develop and implement in Python and Spark.
- Implement Kafka-based CDC patterns for real-time and near-real-time ingestion.
- Develop data lineage, dependency tracking, and modular adapter code.
- Configure Snowflake Horizon external tables for zero-copy reads.
- All data hub tables are to be written once using Delta Lake with Iceberg UniForm enabled… readable by all target consumers without conversion.
- Implement and certify Delta Sharing endpoints for Databricks consumers.
- Build governed access layer components: RBAC, connector registry entries, tenant-scoped authorization.
- Align semantic layer models with LookML and KPI catalog definitions.
- Collaborate with cross‑functional teams to deliver end‑to‑end features.
- Troubleshoot issues across the full stack and contribute to code quality.
Skills/Experience:
- 6+ years of proven enterprise-level experience in Python & Spark
- Advanced experience in GCP BigQuery
- Strong working knowledge of Apache Iceberg, Delta Lake, Iceberg UniForm
- Experience with Delta Sharing; Kafka / CDC pipelines
- Specific work experience in Snowflake Horizon Catalog; Databricks Unity Catalog
- Solid experience with Data lake architecture & ingestion pipeline design
- Excellent Communication skills and the ability to work cohesively with multiple teams.
Preferred Experience – Nice to Have
- Prior delivery in enterprise SaaS, media, or advertising technology.
- Active daily use of AI-assisted development tools (Claude Code preferred).
As published by lever
Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, Twitter URL, GitHub URL, Portfolio URL, Other website, Indeed URL
- If hired, when will you be able to start?
- How many years do you have working in the enterprise with stream-processing systems: Kafka, Nifi, Storm, Spark-Streaming, etc? written answer
- Do you have strong knowledge of designing and developing in Python & Spark? Please be specific to which ones you have worked with. written answer
- Please confirm what specific hands-on experience with ETL techniques and frameworks you have working in the enterprise? written answer
- Please confirm what specific hands-on experience you have working with GCP (Big Query) and at what company? written answer
- Have you worked remote before? choose one
- Will you now or anywhere in the near future require assistance with sponsorship? choose one
- What are your yearly salary expectations working remote?