Data Engineering Manager, Analytics
hace 3 días
**Data Engineering Manager, Analytics (Ads Infrastructure) Responsibilities**:
- Proactively drive the vision for BI and Data Warehousing across a product vertical, and define and execute on a plan to achieve that vision.
- Define the processes needed to achieve operational excellence in all areas, including project management, data quality and system reliability.
- Build a high quality BI and Data Warehousing team and design the team to scale.
- Build cross-functional relationships with Data Scientists, Software Engineers, Production Engineers and Technical Project managers to understand how data can help inform product direction.
- Drive the design, building, and launching of new data models and data pipelines in production.
- Manage development of data resources and support new product launches
- Drive data quality across the product vertical and related business areas.
- Manage the delivery of high impact dashboards and data visualizations.
- Define and manage SLA’s for all data sets and processes running in production.
**Minimum Qualifications**:
- Experience in SQL or similar languages and development experience in at least one scripting language (Python, Perl, etc.).
- Experience working with a MapReduce or an MPP system.
- 3+ years of management experience.
- Communication and leadership experience, with experience initiating and driving projects.
- BA/BS in Computer Science, Math, Physics, or other technical field.
- 3+ years of experience in custom or structured (Informatica/Ab Initio/Talend/Pentaho, etc.) ETL design, implementation, and maintenance.
- Experience scaling and managing 5+ person teams.
- 6+ years of industry experience in Business Intelligence and Data Warehousing.
- Experience leading data driven discussions & designing scalable data platforms for analytics.
- Experience analyzing data to identify deliverables, gaps and inconsistencies.
**Preferred Qualifications**:
- Experience in Ads Technology and Experimentation via A/B testing.
- Experience in developing data solutions for Machine Learning Systems
- Experience with large volume data sets, Hive/Presto/Spark and data visualization tools.
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