Senior Data Engineer

24 days ago

Miami, FL, USA or Los Angeles, CA, USASenior

Responsibilities

Design, develop, and maintain scalable batch data pipelines using Airflow, Python, SQL, and dbt.
Build and maintain real-time ingestion pipelines that move event data from Pub/Sub into Cloud Storage and BigQuery using Dataflow.
Build and optimize dimensional data models and analytics warehouse workflows for reporting, analytics, machine learning, and product initiatives.
Own data pipelines end to end through design, development, testing, deployment, and production support.
Ensure data quality, integrity, security, and availability across the data ecosystem.
Partner with Product Managers, Data Scientists, Analysts, and Engineering teams to translate business requirements into technical solutions.
Contribute to data architecture discussions, long-term platform strategy, process improvements, and operational efficiencies.
Optimize storage, processing, orchestration, performance, scalability, and cloud costs.
Monitor, troubleshoot, and resolve data platform issues and contribute to incident reviews.
Implement best practices for data governance, observability, testing, and CI/CD.
Mentor junior data engineers, contribute to architectural decisions and engineering best practices, and participate in technical interviews.

Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related technical field.
7+ years of experience in Data Engineering, Data Warehousing, or Big Data environments.
Advanced proficiency in Python and SQL.
Hands-on experience building and owning production-grade, end-to-end data pipelines, including real-time or near-real-time pipelines.
Strong experience with Google Cloud Platform, including BigQuery, Cloud Storage, Pub/Sub, Dataflow, and Cloud Composer; AWS experience is also considered.
Experience with workflow orchestration and transformation tools such as Airflow and dbt.
Strong understanding of relational and dimensional data warehousing practices.
Experience with Infrastructure as Code tools such as Pulumi or Terraform.
Solid understanding of distributed data processing frameworks, including Spark.
Strong analytical, troubleshooting, and problem-solving abilities.
Preferred qualifications include supporting machine learning and data science workflows, familiarity with data observability and data quality frameworks, and experience with CI/CD practices.
Experience in subscription-based, media, streaming, or digital product organizations is preferred.
Must be authorized to work in the United States on a full-time basis; employment and education will be verified.

PTO, tuition reimbursement, wellness programs, and employee support programs.
401(k), life insurance, and other insurance plans.
Medical, dental, and vision coverage options.
The position requires current authorization to work full-time in the United States; no remote or office arrangement is stated.