Senior Manager, Data Engineering

The Challenge

As Senior Manager, Data Engineering and Platform you will be a people leader of a team of Data Engineers responsible for creating platform and data engineering architecture for business intelligence and data science solutions. This role will have a substantial focus on coaching and people management alongside a strong individual contributor component in hands-on data engineering.

Your Mission

You will work closely with other team members like data architects and business analysts to understand what the business is trying to achieve, move data from source to target, and design optimal data models.

• Develop and adhere to standard methodologies: platform architecture, data engineering, data quality, analysis, validation to ensure the team provides quality work to company and build trust with analytics solutions.
• Propose and socialize data architecture to enable OneTrust business analytics.
• Drive technical conversations with stakeholders to explore all facets of problem and solution.
• Solve immediate production issues with an eye to solve things for long-term.
• Work with vendors and security teams for appropriate data controls.
• Partner with cross functional teams to align on product usage data, business data, development standards and overall data strategy.
• Design and build facts, dimensions, snapshots, SCDs in Snowflake/Databricks using dbt/airflow/elt tools. Maintain robust, trusted data models.
• Hire and retain top data engineering talent.
• Assist in the growth of each employee through coaching and career development activities.

You Are

Seeking someone who can leverage a deep technical understanding of data engineering, data warehousing, and data architecture while leading and motivating an on-call team of engineers in a challenging, dynamic environment.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 10+ years of professional data engineering experience. Acting as an incident commander during critical outages.
• Extensive experience with data architecture, tool evaluations, data modeling, and building ETL pipelines.
• Experience working in high-growth, fast-paced environment.
• Experience working with distributed teams across geos.
• Experience evaluating new data engineering tools and enriching existing architecture.
• Strong data warehouse knowledge and ability to write complex SQL for processing raw data, data validation, and QA.
• Experience with Python and manipulation of various data formats for extraction and transformation.

Extra Awesome

• Experience using AI in a data engineering context
• Experience managing distributed teams across geos.
• Experience in designing Data Warehouse: ACV, ARR, Event History, Identity Resolution, Deferred Revenue, Revenue Metrics, Experimentation, Audience
• Growth Learner: Desire to continue to learn about the future architecture of data technologies.