Data Engineer

About The Role The Data Engineer designs and operates the data infrastructure that powers product analytics, business reporting, and operational decision-making. The role owns reliable batch and streaming pipelines across application, transactional, and third-party data sources. The Data Engineer designs and operates the data infrastructure that powers product analytics, business reporting, and operational decision-making. The role owns reliable batch and streaming pipelines across application, transactional, and third-party data sources. You will work with analytics engineers, data analysts, software engineers, and product teams to turn complex data requirements into well-modeled, observable datasets. The team values strong data contracts, practical documentation, and systems that remain trustworthy as volume and complexity grow. Key Responsibilities Design, build, and maintain batch and streaming data pipelines using Python, SQL, Apache Airflow, and cloud-native services Develop scalable ELT workflows that ingest data from application databases, APIs, event streams, and SaaS platforms into Snowflake, BigQuery, or equivalent warehouses Model curated datasets using dimensional modeling principles and dbt, with clear ownership, documentation, and downstream usage standards Implement data quality checks, schema validation, lineage, and monitoring to identify freshness, completeness, and accuracy issues before they affect reporting Optimize warehouse queries, pipeline execution, and storage patterns to improve reliability and control infrastructure costs Partner with analysts and stakeholders to define metrics, data contracts, and source-of-truth tables for product and business reporting Review code, improve testing and deployment workflows, and contribute to engineering standards for version control, CI/CD, and production operations What We Are Looking For 3–8 years of experience in data engineering, analytics engineering, or a closely related software engineering role Strong SQL and Python skills, including experience writing maintainable transformations, tests, and production automation Hands‑on experience building and operating data pipelines with tools such as Airflow, Dagster, Kafka, Spark, or equivalent technologies Proficiency with a modern cloud data warehouse such as Snowflake, BigQuery, Redshift, or Databricks Experience with dbt or comparable transformation and data modeling frameworks, including dimensional modeling and incremental processing Bachelor's degree in computer science, engineering, mathematics, statistics, or a related technical field, or equivalent practical experience Bonus: experience with Terraform, Kubernetes, streaming architectures, data observability tools, and platforms such as AWS, GCP, or Azure #J-18808-Ljbffr