Senior Data Analytics Engineer

The trucking industry is the backbone of the global economy. More than 70% of what we consume in the U.S. is moved by trucks. Those trucks are powered by over 3.5 million drivers per year and  create over $700B in annual revenue. Trucking is a massive industry but it is a traditional industry and like many traditional industries, it is ripe for innovation.

CloudTrucks is building the operating system for trucking and is the first platform specifically designed to empower truck drivers. Our all-in-one, “business in a box” solution optimizes and automates operations and accelerates cash-flow for drivers, so they can focus on building their business.

The Data Analytics team at CloudTrucks owns all business reporting end-to-end, and develops both internal and customer-facing, data analytics products in collaboration with Product development teams.

As a Senior Data Analytics Engineer, you’ll be responsible for maintaining and scaling our Data Infrastructure. You’ll also have the opportunity to collaborate with teams across the company, and support them with their data needs, from ingesting new data sources, to help them design the proper data architecture for more intricate features and reporting.

Examples of projects that you may work on

• Help engineering teams ingest data from 3rd party sources, in order to build pipelines that power user facing features.

• Collaborate with our Operations Data Analyst and Operations stakeholders to develop efficient and scalable data-driven solutions for their most pressing operational reporting.

• Work with our Data Analytics Engineer to audit and identify opportunities to scale our Data Infrastructure

• Work with Machine Learning Engineers and Data Scientists to support their ETL and data pipeline needs.

• Leverage existing tooling or introduce new tooling that helps Data Analysts version control their analyzes and iterate on it with an analytics as code mindset

Responsibilities

• Build, audit, and evolve data ingestion processes, always with performance and scalability in mind - we use a mix of Google Cloud Services, Airflow and Segment

• Evolve and scale our data warehouse

  • Add additional data, maintain an organize our data warehouse

  • Apply engineering best practices to our data transformation layer - we use Dataform from Google Cloud Services

  • Improve the efficiency of our most demanding transformation queries with performant SQL code

  • Enable operational analytics by syncing data to 3rd party tools, "closing the loop" in data circulation

• Enable operational analytics by syncing data to 3rd party tools. We have several integrations with 3rd party systems like Salesforce, Marketo, Heap and Segment

• Be the keystone for self-service analytics and data visualization

  • Manage data visualization in Looker; build and own mission critical dashboards

  • Support the organization to answer questions with data through training, tooling, process and your ingenuity

• Collaborate across the company to ensure the right data is available for all projects

• Define, drive and own service level agreements for customer facing, as well as internal, data analytics products

• Champion data best practices across engineering, especially around efficiency, coding standards, data observability, data security and operations.

• Own the Data Infrastructure roadmap, and work with the Head of Data Analytics to define the strategy for the data warehouse and data infrastructure

• Collaborate with software engineers on data needs for Machine Learning pipelines

What we are looking for

• 5+ years of experience working with data warehouses: building, monitoring, maintaining and scaling ETL pipelines, with a focus on data quality, integrity and security

• Expertise in software engineering principles - version control, code reviews, testing, CI - as well as git and command line interfaces

• Expertise in writing complex, efficient and DRY SQL code, as well as handling large data sets, preferably in Python, and identifying and resolving bottlenecks in production systems

• Understanding of data engineering architectures, tools and resources - databases, computation engines, stream processors, workflow orchestrators and serialization formats - especially cloud hosted and managed versions

• An efficient, customer-focused approach to development, pursuing pragmatic solutions to deliver the best results

• Expertise with managing analytics, data engineering & visualization tools, Looker is preferred.<