Senior Data Engineer

As a Senior Data Engineer on the Data Science team, you'll be embedded with data scientists and ML engineers to build the data infrastructure that powers Veho's models, experiments, and operational decisions. You'll spend most of your time in DBT and Databricks designing models, building features, and shipping pipelines that turn raw operational signals into trusted, production-grade datasets the team relies on every day. Orchestration runs on Airflow, with Prefect in places, and you will own these workflows end-to-end. You'll be an owner for data models critical to the business to understand the intersection of data science model quality and real-world performance (e. g., how well do we forecast a driver's stop time, how effective are we at making our middle mile network adhere to our planning).

Responsibilities:

• Design and maintain DBT models that produce trusted datasets, features, and metrics the Data Science team relies on for analysis, experimentation, ML, and reporting.
• Build and operate pipelines in Databricks - PySpark jobs and Delta/Iceberg tables - that turn raw operational events into analysis-ready data.
• Develop deep familiarity with Veho's operations so the datasets, schemas, and models you ship reflect how the business actually works.
• Orchestrate end-to-end data workflows in Airflow (and Prefect where it fits), with SLAs the DS team can count on for daily models, dashboards, and operational decisions.
• Participate in peer code reviews and raise the bar on data quality, testing, and documentation across the team's models.
• Partner with data scientists to scope, design, and productionize feature pipelines and the model-supporting data behind them.
• Optimise DBT and Spark workloads for cost, performance, and reliability as Veho's data volume grows.
• Learning new technologies quickly - nobody comes into this role knowing every piece of the stack.
• You will lean on peers, documentation, and experimentation to grow.

Requirements:

• 5+ years of experience building, testing, and deploying data engineering systems.
• Experience with at least one distributed data system, and the ability to reason about consistency, latency, throughput, and fault tolerance.
• Strong SQL and proficiency with at least one of (Py)Spark, DBT, or Airflow in production.
• Experience with Infrastructure-as-Code systems such as Terraform, AWS CDK, or Pulumi.
• Understanding or a strong interest in supply chain and the data challenges it creates.
• A self-starter who takes initiative, moves fast, and ships while collaborating on big challenges.
• Enthusiastic about working closely with team members to develop creative solutions to novel problems.
• Excellent written and oral communication in English.
• Tech: DBT, Databricks, (py)Spark, Airflow, Prefect, SQL, Iceberg/Delta; familiarity with the broader Veho stack (Kinesis, EMR, Sigma, Pulumi) is a plus.
• Comfortable using modern AI coding assistants (Claude Code, Cursor, Copilot, or similar) and experienced with AI-native workflows - prompting, agentic tooling, evaluations, retrieval - and willing to bring them into your data engineering practice.
• A plus: Demonstrated, measurable success building with LLMs, evaluating model outputs, or integrating AI into data pipelines or internal tooling.

Nice to Have:

• Experience with data lakehouse architectures (Iceberg or Delta).
• Familiarity with Kinesis or other streaming systems in production.
• Exposure to MLOps workflows or supporting ML/AI teams.
• Prior experience working with US-based engineering teams across time zones.