Data Engineer

Looking for a mid-to-senior Data Engineer to build and own the data infrastructure that powers our platform. You'll design and maintain the pipelines, storage systems, and data models that our analytics, data science, and product teams rely on every day. This is a hands-on engineering role with real ownership over how data moves and scales across the company. We're looking for an experienced data engineer to architect, build, and maintain the systems that make our data trustworthy, accessible, and fast. You'll partner closely with data science, analytics, product, and engineering teams to understand their needs and build the pipelines and infrastructure to support them. You'll also help set standards for data quality, governance, and best practices as the team scales. Responsibilities Data Architecture & Pipeline Development Design, build, and maintain scalable, production-grade data pipelines (batch and streaming). Architect and optimize data storage solutions, including data warehouse and/or data lake infrastructure, for analytics and operational use. Own ETL/ELT processes end to end, using tools such as Apache Airflow, dbt, and Apache Spark. Design data models and schemas that balance performance, cost, and usability for downstream teams. Reliability, Quality & Governance Establish and enforce data quality checks, monitoring, and alerting across pipelines. Define and maintain data governance standards, including access controls, documentation, and lineage. Troubleshoot and resolve data pipeline failures and performance bottlenecks. Optimize query and storage performance as data volume and complexity grow. Cross-Functional Partnership Work closely with data science, analytics, product, and engineering teams to translate business needs into data infrastructure. Support machine learning and predictive modeling initiatives by delivering clean, well-structured, production-ready data. Partner with the broader engineering org on system design decisions that touch data infrastructure. Mentor junior team members and help set technical standards and best practices for the data function. Desired Skills & Qualifications Experience: 5+ years of professional experience in data engineering or a closely related role, with a track record of building and owning production data systems. Technical Skills: Advanced proficiency in Python and SQL. Deep experience with ETL/ELT design and orchestration tools (e.g., Apache Airflow, dbt). Experience with distributed data processing frameworks (e.g., Apache Spark). Strong experience with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and cloud platforms (AWS, GCP, or Azure). Solid understanding of data modeling, schema design, and performance optimization for analytics workloads. Experience implementing data quality, monitoring, and governance practices at scale. Familiarity with version control and CI/CD practices as applied to data pipelines. Other Qualifications: Strong problem-solving skills with close attention to data accuracy and system reliability. Ability to communicate technical decisions clearly to both technical and non-technical stakeholders. Comfortable owning projects independently and setting technical direction for the data function. Experience mentoring other engineers is a plus.