About the Role:

At The New York Times, data powers decisions across the entire company. The Analytics Data Products team builds the foundational data products and pipelines that make that possible, and we're looking for a Data Engineer to help us build them. You'll own and enhance the data pipelines and core, reusable data products that partner teams across the company depend on to unlock analytics for their most important questions. You'll work hands-on across our hybrid cloud architecture (AWS and GCP) and contribute to the platform that delivers trusted data products company-wide spanning multiple business domains. You'll join a collaborative team that invests in your growth. Reporting to the Senior Engineering Manager of Analytics Data Products, you'll take ownership of pipelines and products, learn from experienced engineers, and grow your impact across the company.

This is a hybrid role in our New York City headquarters.
Responsibilities:

• Design, model, and implement complex data 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 complex data processing within the Lakehouse, ensuring outputs meet strict SLAs and quality standards.
• Collaborate with Data Analysts and other consumers to define requirements and translate them into scalable data models suitable for analytic use cases.

• Manage physical data storage across both GCP (GCS, BigQuery, Cloud Composer) and AWS (S3, Glue, Athena, EMR).
• Choose optimal file formats such as Parquet and Iceberg, and design 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.
• 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) programs across the Lakehouse environment.

Basic Qualifications

• 2+ years of full-time professional, hands-on experience with Software Engineering in a data context or equivalent experience
• Strong proficiency in Python for scripting and data manipulation
• Strong proficiency in SQL and demonstrable experience with complex, production-level data modeling (preferably dimensional modeling, Kimball, OBT, or Data Vault)
• Demonstrated experience owning data pipelines and products end-to-end through the full SDLC
• Hands-on experience with a Cloud Data Warehouse (BigQuery, Snowflake, DataBricks)
• Familiarity with foundational 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
• Knowledge of PySpark or other Spark APIs
• Experience with advanced Lakehouse file formats like Iceberg or Delta Lake
• Familiarity ensuring data product SLAs and quality standards, integrating advanced testing, quality checks, and monitoring into the CI/CD pipeline

REQ-019488

#LI-hybrid

The annual base pay range for this role is between:

$110,000-$130,000 USD

For roles in the U.S., dependent on your role, you may be eligible for variable pay, such as an annual bonus and restricted stock. Benefits may include medical, dental and vision benefits, Flexible Spending Accounts (F.S.A.s), a company-matching 401(k) plan, paid vacation, paid sick days, paid parental leave, tuition reimbursement and professional development programs.

For roles outside of the U.S., information on benefits will be provided during the interview process.

We're excited to learn more about you and your experience. To keep our hiring process as fair and authentic as possible, we ask that you submit your own work and not use GenAI tools to generate substantive content during the application and interview process.

If you're an Engineering candidate, we'll let you know what specific GenAI tools you are permitted to use for your technical assessment.