Lead Data Engineer, Scalable Analytics for Logistics

Riverstone Logistics

Rock Hill (SC)

On-site

USD 110,000 - 160,000

Full time

4 days ago

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Job summary

Riverstone Logistics is seeking a Senior Data Engineer to build reliable data pipelines and models that power self-serve analytics across our logistics platform. You will ingests data, design dbt models, and ensure governance, quality, and cost efficiency in a fast-paced environment.

You will work across ingestion, transformation, warehousing, and analytics delivery, collaborating with Analytics, Product, Operations, and Finance to turn business questions into production-ready insights.

Qualifications

5+ years of experience in data engineering or a closely related role.
Advanced SQL and Snowflake experience.
Production dbt experience—modeling, testing, macros, and CI.
Proficient Python for pipeline work, API integrations, and automation.
Experience with ELT tooling (Fivetran or equivalent) and reverse ETL (Hightouch or equivalent).
Solid grounding in dimensional modeling and warehouse design.
Track record building data quality and observability into pipelines.
Git-based workflows and version control discipline.
Strong collaboration and communication skills.
Experience with Omni, Looker, or a similar semantic-layer BI tool preferred.
Experience with logistics, transportation, or supply chain data (TMS, WMS, EDI, telematics) preferred.

Responsibilities

Build and maintain ingestion pipelines into Snowflake using Fivetran and custom Python where connectors fall short.
Design and own dbt models—staging through marts—with clear tests, documentation, and ownership.
Model operational data to reflect real-world logistics workflows, not just source-system structure.
Contribute to improving warehouse design, performance, and maintainability.
Design and implement data integrations with third-party and internal systems (e.g., TMS, WMS, partner platforms).
Operationalize data through Hightouch, syncing modeled data back into the systems where work happens.
Deliver curated, well-defined datasets that power Omni dashboards and self-serve reporting.
Ensure pipelines and syncs are reliable, scalable, and maintainable in production.
Own Snowflake performance and cost—warehouse sizing, query tuning, storage strategy.
Establish data quality standards: testing, monitoring, alerting, and lineage so issues surface before stakeholders find them.
Set and enforce governance practices—access controls, naming conventions, shared definitions, documentation.
Participate in code reviews and contribute to shared engineering standards and best practices.
Identify and address technical debt and pipeline inefficiencies.
Partner with Analytics, Product, Operations, and Finance on data model design and execution.
Support investigation and resolution of data issues affecting internal teams and customers.
Provide technical input to improve workflows and reduce operational friction.
Communicate clearly on technical trade-offs, timelines, and risks.