Manager, Data Operations

Dotdash Meredith•New York, NY

5d•$190,000 - $205,000•Hybrid

About The Position

The Manager, Data Operations leads the design, reliability, and day-to-day execution of the company's core data pipelines and analytics infrastructure while directly managing a team of Data Engineers and Analytics Engineers. This role combines hands-on technical leadership with people management accountability, ensuring that data systems are scalable, trusted, well-documented, and aligned with business priorities. The role is responsible for setting technical direction, prioritizing team work-streams, developing talent, and ensuring cross-functional alignment across Analytics, Product, and Technology. This is both a strategic leadership role and a hands-on operational steward for the company's data platform.

Requirements

7+ years of experience building and optimizing data pipelines using Python or similar programming languages.
5+ years of advanced SQL experience working with relational and distributed data systems (e.g., Hive, BigQuery).
Hands-on experience with dbt for data transformation and pipeline management.
2+ years of experience leading or mentoring a team of data or analytics engineers, or owning end-to-end delivery of large-scale data products.
Strong leadership and talent-development skills
Excellent communication across technical and business audiences
Strong prioritization, decision-making, and documentation discipline
Familiarity with cloud data platforms such as BigQuery, Snowflake, or AWS/GCP.
Bachelor's degree in Computer Science, Engineering, or a related field.

Nice To Haves

Experience with data visualization tools such as Looker, Tableau, or similar is a plus.

Responsibilities

Own the design, reliability, and scalability of ingestion, transformation, and storage pipelines
Ensure ELT/ETL processes run with high uptime and strong data quality
Guide orchestration, monitoring, and performance optimization
Reduce operational fragility by promoting shared patterns and automation
Oversee development and maintenance of clean, tested, well-documented data models (e.g., dbt)
Define standards for modeling and semantic consistency
Ensure downstream BI and analytics consumers have trusted, well-structured datasets
Establish clear metric definitions and semantic models
Partner with Governance and Security teams on lineage, metadata, and access controls
Reduce metric inconsistencies and reporting ambiguity
Ensure compliance with privacy and security standards
Serve as a data advisor to Analytics, Product, and business leadership
Translate business priorities into technical roadmaps
Communicate complex technical concepts and trade-offs clearly to technical and non-technical audiences
Prioritize investments based on measurable business impact
Manage and mentor a team of data engineers responsible for building and maintaining core data models, dashboards, and scalable data products.
Collaborate with stakeholders to understand business needs, gather requirements, and deliver high-quality data solutions that support reporting and decision-making.
Design, implement, and optimize performant, well-documented data models that enable self-service analytics and strategic initiatives.
Define and champion engineering best practices around code quality, documentation, version control, and testing within analytics workflows.
Lead or support ad-hoc data analyses to uncover insights and drive business strategy.
Stay up to date on industry trends and new technologies to continuously improve our data architecture, tooling, and processes.