Senior Data Pipeline & Connectivity Engineer (Databricks)
Location: Charlotte, NC (Hybrid Preferred) | Open to Remote for Top Talent
Comp: $140K–$180K Base + Bonus + Equity
Company Overview
We are a rapidly growing, private equity-backed services platform operating across dozens of acquired business units. Our environment consists of 50+ disconnected systems and ERPs, each operating independently across separate networks.
We are building a modern, Databricks-first data and AI platform to unify operations, enable real-time insights, and power next-generation AI use cases.
The Opportunity
This is a foundational engineering role focused on solving one of the hardest problems in modern data: connecting fragmented systems into a scalable, governed data platform.
You will own the design and build of data pipelines, ingestion frameworks, and connectivity patterns that bring together disparate environments into Databricks—laying the groundwork for data products, analytics, and AI.
This is not a support role. This is a hands-on builder role for someone who wants to architect and implement at scale.
What You’ll Own
? Connectivity & Data Ingestion
- Design and implement robust ingestion pipelines across highly fragmented environments (multiple ERPs, isolated networks, legacy systems)
- Build connectivity across:
- ERP systems (e.g., Great Plains, SAP, Infor, custom systems)
- APIs, flat files, streaming sources, and third-party platforms
- Solve for network isolation challenges and inconsistent data access patterns
?? Databricks Pipeline Engineering
- Build and optimize Databricks-based pipelines using:
- PySpark
- Delta Lake
- Structured Streaming / batch frameworks
- Implement scalable ingestion patterns aligned to medallion architecture (Bronze ? Silver ? Gold)
- Ensure data quality, reliability, and performance across pipelines
? Platform & Architecture Contribution
- Partner with Data Architect / Leadership to:
- Define ingestion frameworks and reusable pipeline patterns
- Standardize data models across business units
- Enable scalable onboarding of newly acquired companies
? Data Product Enablement
- Deliver clean, reliable datasets that power:
- Operational reporting
- Power BI / visualization layers
- AI / ML and LLM-based use cases
? Governance, Quality & Reliability
- Implement:
- Data validation and monitoring
- Logging and observability
- Secure and compliant data movement
What You Bring
? Must-Have Experience
- 5–10+ years in data engineering / pipeline engineering
- Deep hands-on experience with:
- Databricks (required)
- PySpark
- Delta Lake
- Proven experience building:
- Data pipelines across multiple disconnected systems
- Scalable ingestion frameworks
? Strongly Preferred
- Experience in complex, multi-entity environments (PE-backed, M&A, roll-ups)
- ERP data integration experience:
- Great Plains, SAP, Infor, NetSuite, etc.
- Experience with:
- AWS or Azure data ecosystems
- API integrations and event-based pipelines
- Data orchestration tools
? Ideal Profile
- Builder mindset — thrives in greenfield + messy environments
- Comfortable operating with incomplete data and evolving requirements
- Can own problems end-to-end, not just execute tickets
- Balances speed with scalable architecture
Why This Role is Different
- You are not maintaining pipelines — you are building the foundation
- Direct impact on enterprise-wide data consolidation strategy
- Clear path into:
- Data Platform Lead
- Data Architecture
- AI / Data Product leadership
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