Data Engineer

Our roster has an opening with your name on it! We are looking for a Data Engineer to join our growing data engineering team and help build the pipelines and infrastructure that power analytics, machine learning, and business decision-making across the company. In this role, you’ll contribute to the design, development, and maintenance of reliable data systems while collaborating with stakeholders to support high-impact data use cases. The ideal candidate is a strong technical contributor who enjoys working with data at scale, solving practical problems, and continuously learning in a fast-paced environment. If you’re excited by this challenge and want to work within a dynamic company, then we’d love to hear from you.

Everyone on our team has a part to play. You will build and maintain data pipelines, design, build, and maintain scalable batch and streaming data pipelines to support analytics and business operations. You will write clean, efficient, and well-documented code using tools like Python, SQL, and Spark, ensuring data is reliable, accurate, and delivered in a timely manner. Collaboration is key, as you will work with data analysts, data scientists, and product managers to understand requirements and deliver actionable data solutions. You will translate business questions into engineering tasks and contribute to technical planning, participating in code reviews, sprint planning, and retrospectives as part of an agile team. Additionally, you will monitor data pipelines and troubleshoot issues in a timely, systematic manner, implementing data quality checks and contributing to observability and testing practices. Documenting data sources, transformations, and architecture decisions will support long-term maintainability.

Experience in data engineering, analytics engineering, or software engineering with a focus on data. Strong SQL skills and familiarity with at least one programming language (e.g., Python, Java, or Scala). Hands-on experience with modern data tools such as Databricks, Airflow, DBT, Spark, or Kafka. Understanding of data modeling concepts, data warehousing, and ETL/ELT best practices. Experience working with cloud-based data platforms (AWS, GCP, or Azure). Preferred qualifications include experience supporting BI, analytics, or data science teams, familiarity with version control, CI/CD, and collaborative development workflows, exposure to data governance, privacy, or compliance practices, and eagerness to learn new technologies.