Data Engineer

Strider Technologies•South Jordan, UT

39d•Hybrid

About The Position

Strider Technologies is building its first internal data platform - and we're looking for a Data Engineer to help design, implement, and scale it. You'll work alongside the Director of Analytics to establish core data infrastructure, pipelines, and integrations that power analytics, business intelligence, and data science across the company. This is a foundational role: you'll be a key builder and early technical voice in how data is engineered, modeled, and served to business users. As our first Data Engineer, you'll help us set standards for data governance, reliability, and performance, while also mentoring future team members as we grow.

Requirements

5+ years of experience in data engineering or analytics engineering roles.
Strong proficiency with SQL and Python (or Scala).
Hands-on experience with modern cloud data warehouses (Databricks, Snowflake, BigQuery, or Redshift).
Familiarity with tools in the modern data stack (e.g., dbt, Airflow, Fivetran, Dagster).
Proven ability to manage and transform large, complex datasets across structured and unstructured formats.
Solid understanding of data warehousing concepts, dimensional modeling, and Git-based version control.
Experience leveraging AI-assisted development tools and enthusiasm for scaling their use internally.
Comfortable working in fast-paced environments and collaborating across technical and non-technical teams.

Responsibilities

Design, implement, and maintain scalable data pipelines (ETL/ELT) in a modern cloud data warehouse (e.g., Databricks, Snowflake, BigQuery, or Redshift).
Ingest, transform, and integrate internal system data (e.g., CRM, product, finance) and external vendor datasets.
Prepare and process structured and unstructured data to support analytics, business intelligence, and automation use cases.
Model and optimize datasets using Lakehouse or warehouse-based architectures for downstream consumption.
Collaborate closely with analysts and cross-functional partners to build production-grade data assets.
Champion and implement best practices for data quality, observability, and lineage.
Partner with cloud infrastructure teams (AWS, Azure, or GCP) to ensure scalable, secure, and cost-efficient systems.
Explore and deploy AI-enabled automation tools and workflows across the business.
Advocate for productivity-boosting technologies such as ChatGPT, Copilot, Cursor, and dbt-assist.
Help document standards, develop reusable components, and mentor junior data engineers.