Data Engineer, Analytics Data Products

The mission of The New York Times is to seek the truth and help people understand the world. That means independent journalism is at the heart of all we do as a company. It’s why we have a world-renowned newsroom that sends journalists to report on the ground from nearly 160 countries. It’s why we focus deeply on how our readers will experience our journalism, from print to audio to a world-class digital and app destination. And it’s why our business strategy centers on making journalism so good that it’s worth paying for. About the Role: We are part of a New York-based technology organization with a remote-friendly workplace that includes engineers around the world. We value transparency and openness, learning, community, and continuous improvement. Check out the Times Open blog , which is written by engineers and other technical team members, and follow @nytdevs on Twitter to see what we're up to. Responsibilities: Design, model, and implement complex ELT/ETL pipelines for the cleansed and curated data layers in the medallion architecture, taking full ownership of the data product's structure, partitioning, documentation, and performance characteristics. Develop advanced data transformations using dbt (data build tool) for relational data modeling and PySpark for large-scale data processing within the Lakehouse, ensuring outputs meet strict Service Level Agreements and quality standards. Collaborate across teams to define requirements and translate them into robust and scalable data models suitable for analytic consumption. Manage the physical data storage across both GCP and AWS, selecting optimal file formats and designing efficient partitioning and clustering strategies. Administer and tune Spark compute resources (e.g., Dataproc, EMR, or managed services) to optimize job execution time and cost. Own core components of our centralized analytics environment, specifically focused on Hex, integrations, and the methods of data exposure and access controls; and support data activation strategies, ensuring seamless data consumption by analytic tools. Optimize user queries and access patterns to maintain platform performance and cost efficiency. Implement centralized data quality checks and observability mechanisms within the data pipeline to proactively identify and resolve data issues. Contribute to the implementation of metadata management, data lineage, and role-based access control (RBAC) initiatives across the Lakehouse environment. Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world. Basic Qualifications: 2+ years of hands-on experience in a Data Engineering, Data Warehousing, Analytics Engineering or equivalent role Proficiency in SQL and experience with complex, production-level data modeling (dimensional modeling, Kimball, OBT, or Data Vault) Demonstrated experience designing, developing, and deploying end-to-end data products through the full Software Development Lifecycle Experience with a Cloud Data Warehouse, like BigQuery Proficiency in Python for scripting and data manipulation, including knowledge of PySpark or other Spark APIs Familiarity with cloud services and data storage components in at least one major cloud provider (GCP or AWS) Experience with workflow orchestration tools (e.g., Airflow, Cloud Composer, or Prefect) and version control systems (Git) Preferred Qualifications: Experience operating in a dual-cloud environment (GCP/AWS) Experience with Infrastructure-as-Code (IaC) tools like Terraform Experience with advanced Lakehouse file formats like Iceberg or Delta Lake Familiarity with experimentation or A/B testing platforms and the data required to support them Experience in data product quality standards through integration advanced testing, quality checks, and monitoring into the CI/CD pipeline REQ-019489 #LI-hybrid The annual base pay range f ... (truncated, view full listing at source)