Data Engineer

The Data Engineer will design and operate the data infrastructure that powers product analytics, operational reporting, and machine learning. The role covers batch and streaming pipelines, warehouse modeling, data quality, and reliable delivery of trusted datasets to analysts and downstream applications.

Working with analytics engineers, data scientists, and software engineers, the role will turn high-volume event and operational data into scalable, well-documented data products. The team values strong engineering fundamentals, measurable pipeline reliability, and pragmatic decisions about cost, performance, and maintainability.

Key Responsibilities

• Design and maintain batch and streaming data pipelines using Python, SQL, and Apache Airflow or equivalent orchestration tools
• Build dimensional and analytical data models in Snowflake, BigQuery, or Databricks to support reporting, experimentation, and product analytics
• Develop reliable ingestion workflows from application databases, APIs, event streams, and third-party systems using technologies such as Kafka, Fivetran, or Spark
• Implement data quality checks, schema management, lineage, and observability using tools such as dbt, Great Expectations, Monte Carlo, or equivalent platforms
• Optimize warehouse queries, storage layouts, and pipeline execution to improve performance while controlling infrastructure costs
• Partner with analysts and business stakeholders to define source-of-truth metrics, delivery requirements, and usable data contracts
• Review code, document architectural decisions, and improve CI/CD, testing, monitoring, and incident response practices for the data platform

• 3–8 years of experience in data engineering, analytics engineering, or a closely related software engineering role
• Strong Python and SQL skills, including experience building production-grade transformations, APIs, and data processing workflows
• Hands-on experience with a modern cloud data stack, including AWS, GCP, or Azure and a warehouse or lakehouse such as Snowflake, BigQuery, Redshift, or Databricks
• Experience with orchestration and transformation tools such as Airflow, Dagster, Prefect, dbt, Spark, or equivalent technologies
• Understanding of data modeling, distributed systems, ETL/ELT design, schema evolution, partitioning, and pipeline reliability
• Bachelor’s degree in computer science, engineering, mathematics, or a related technical field, or equivalent practical experience
• Bonus: Experience with Kafka or other streaming platforms, Terraform, Kubernetes, data catalogs, CDC tools, or production machine learning data pipelines