Senior Data Engineer

Audacy; New York (NY); On-site; USD 120,000 - 150,000; Full time

Job summary

Audacy is seeking a Senior Data Engineer in New York to join our Data Engineering team. You will be responsible for building and optimizing data infrastructure that supports business insights and product innovation. Strong skills in Python, SQL, and cloud technologies like GCP and AWS are required.

The role involves designing data pipelines, troubleshooting issues, and mentoring other engineers. The ideal candidate will have 8+ years of experience in data engineering and a degree in Computer Science or related field.

Qualifications

• 8+ years of professional data engineering experience.
• 6+ years of experience with Python and SQL.
• Extensive experience with cloud data warehouses.

Responsibilities

• Design and develop data pipelines using cloud technologies.
• Build and optimize data models and warehouses.
• Collaborate with analysts and stakeholders to solve data problems.

Skills

Data engineering

Python

SQL

Cloud technologies (GCP, AWS)

Airflow

Data modeling

ETL/ELT systems

Data governance

Education

Degree in Computer Science or related field

Tools

BigQuery

Snowflake

Google Data tools (DataFlow, DataForm, Pub/Sub, GCS)

Job description

Job Title: Senior Data Engineer

Department: Digital

Reporting To: Manager, Data Engineering

Employment Type: Full-Time

Overview

Audacy is looking for a Senior Data Engineer to join our dynamic Data Engineering team. In this role, you’ll be at the forefront of building and optimizing scalable data infrastructure that supports critical business insights, product innovation, and operational excellence. You’ll collaborate with other engineering teams, analytics, and product teams to design and drive data-forward solutions across a massive-scale digital audio ecosystem.

Whether you’re designing data pipelines, optimizing data models, or ensuring the reliability and performance of our data platform, you’ll play a key role in shaping how Audacy utilizes big data on the magnitude of billions of rows to unlock insights, enhance decision‑making, and drive successful business outcomes. We are looking for a technical leader who values focus, curiosity, and collaboration who can mentor other data engineers—and we offer an environment that supports your growth while respecting your work‑life balance.

Responsibilities

• Design and develop batch and streaming data pipelines using cloud technologies (GCP, AWS, BigQuery, Snowflake, etc). GCP and BigQuery experience preferred.
• Build, maintain, and optimize data models and dimensional warehouses to support analytics and BI tooling as well as other operational workflows.
• Design and develop robust ETL/ELT processes for ingesting, transforming, and validating terabytes of structured data.
• Troubleshoot complex data pipeline issues to determine root cause and corrective actions.
• Research, analyze, recommend, and implement technical approaches for solving complex data transformation and integration problems within a Medallion data warehouse architecture.
• Collaborate with business analysts, data scientists, and stakeholders to understand complex data problems and translate business requirements into technical solutions and a structured backlog.
• Lead and mentor other data engineers to follow the team’s evolving data engineering standards and best practices.
• Analyze, support and improve the performance, reliability, and scalability of our data infrastructure.
• Define and implement data quality, data governance, privacy‑first practices, and validation mechanisms to ensure accuracy and compliance (e.g., GDPR).
• Document pipelines, systems, and workflows to improve our internal data catalog.
• Stay current with emerging technologies and evaluate their relevance in our stack.

Qualifications

• 8+ years of professional data engineering and programming skills building scalable ETL/ELT systems.
• Degree in Computer Science or related field, or equivalent practical experience.
• 6+ years of experience with Python and SQL.
• Advanced analytical problem solving and troubleshooting skills.
• Extensive experience working with cloud data warehouses (e.g. BigQuery or Snowflake), and Google Data tools (DataFlow, DataForm, Pub/Sub, GCS).
• 3+ years of experience with Airflow (Cloud Composer), data modeling (star/relational schema), and test case development.
• Solid understanding of columnar vs. row‑based storage solutions and when to use each (e.g., Snowflake vs. Postgres).
• 3+ years experience writing design documents and breaking work down into a well‑structured backlog.
• Proven ability to work in agile, cross‑functional teams.
• Excellent verbal and written communication skills; a team player who enjoys learning and teaching. Willingness to dig into “why” to help build the right solutions and data products for our business to scale our data capabilities.

EEO

Audacy is an Equal Opportunity Employer. Audacy affords equal employment opportunity to qualified individuals regardless of their race, color, religion or religious creed, sex/gender (including pregnancy, childbirth, breastfeeding, or related medical conditions), sexual orientation, gender identity, gender expression, national origin, ancestry, age (over 40), physical or mental disability, medical condition, genetic information, marital status, military or veteran status, or other classification protected by applicable federal, state, or local law, and to comply with all applicable laws and regulations. Consistent with our commitment to equal employment opportunity, we provide reasonable accommodations to qualified individuals with disabilities who need assistance in applying electronically for a position with Audacy, unless doing so would impose an undue hardship. To request a reasonable accommodation for this purpose, please call 1-610-660-5614. Please note that this phone number is to be used solely to request an accommodation with respect to the online application process. Calls for any other reason will not be returned. Reasonable accommodation requests are considered on a case‑by‑case basis.