Data Engineer

Nederlander Producing Co of America Inc•New York, NY

18d

About The Position

This role focuses on designing, building, and operating scalable data pipelines and data warehouse architecture. The Data Engineer will be responsible for managing data infrastructure, ensuring data quality and reliability, automating processes, and enabling downstream analytics and BI. A key aspect of the role involves supporting dynamic pricing models through robust data infrastructure and modeling.

Requirements

Strong proficiency in SQL and advanced data modeling (dimensional + canonical).
Experience building and operating data pipelines using Python and modern data stack tools (Fivetran, DBT, Hightouch or similar).
Deep experience with Snowflake or similar cloud data warehouses.
Experience working with CRM systems (Salesforce preferred).
Experience managing data infrastructure in AWS or similar cloud environments.
Strong understanding of ETL/ELT design patterns, APIs, and distributed data systems.

Responsibilities

Design, build, and operate scalable data pipelines across Salesforce, Snowflake, and external systems (ticketing, lottery, partners).
Leverage Fivetran and API-based ingestion frameworks to standardize and automate data ingestion from third-party platforms.
Own API, webhook, and file-based ingestion systems (SFTP, third-party feeds), ensuring reliability and observability.
Manage AWS infrastructure supporting data ingestion, processing, and orchestration.
Own Snowflake data architecture, including schema design, transformations, and data modeling.
Develop and maintain transformation layers using DBT, including modular, testable, and well-documented models.
Build canonical datasets and dimensional models supporting analytics, CRM, ticketing, and pricing.
Optimize warehouse performance, cost, and scalability through clustering, partitioning, and query optimization.
Manage schema evolution and deprecation of legacy data structures.
Establish data quality frameworks using DBT tests, monitoring tools, and pipeline validation checks to detect and resolve inconsistencies, duplication, and missing data.
Implement automated validation, monitoring, and alerting across pipelines.
Own data hygiene standards across customer, order, and transaction datasets.
Develop Python-based workflows to automate ingestion, transformation, and operational processes.
Reduce manual workflows through system design, tooling, and pipeline reliability improvements.
Leverage Hightouch to operationalize data by syncing curated datasets back into Salesforce and other business systems.
Partner with third-party developers to maintain ticket lottery systems.
Continuously improve pipeline performance, latency, and failure recovery.
Build and maintain data pipelines supporting core reporting (sales, marketing performance, partner reporting).
Enable downstream analytics and BI through clean, accessible, and well-modeled datasets.
Own the data infrastructure supporting dynamic pricing models.
Design and maintain model-ready datasets across pricing, inventory, and demand signals using Snowflake and DBT.
Partner with internal teams and external consultants to operationalize pricing models within Snowflake.
Enable rapid iteration, backtesting, and deployment of pricing strategies.