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We are looking for a Data Engineer to work within the Ads Data team and support Marketing Science initiatives. You will work closely with engineers and product owners from our Ads and Marketing teams to build a data warehouse that can power measurement solutions for advertisers.
Build robust data infrastructure that not only supports the current Measurement initiatives, but also serves as a data platform on which future Measurement solutions will depend upon.
You will take over the existing Marketing data pipelines and enhance them to improve data quality, accessibility and reliability.
Build a norms database that tracks the performance of various advertising campaigns. A complete understanding of campaign parameters and associated performance will help us make recommendations that can improve advertiser ROI.
Develop scalable experimentation solutions through 3rd party data ingestions, integrations with measurement and data partners, automated scripts for experiment set up validation and metric pipelines for lift measurement.
Partner with Data Scientists, Measurement Researchers and leads in building measurement solutions such as conversion lift studies, brand lift studies, brand insights tools etc
Investigate discrepancies between 1st party and 3rd party mobile conversion data and identify gaps and product changes to improve our conversion coverage
Assist with running and evaluating multi-cell experiments against counterfactuals and wrangle with data issues and biases. Test out various statistical approaches to deal with data insufficiency and under-powered tests.
Build tools that can assist Marketing and Sales teams with creating powerful marketing narratives for advertisers.
Build ETLs and data aggregations that can improve our understanding of ad performance.
What We Can Expect From You:
Bachelor’s degree or above in a quantitative major (e.g., mathematics, statistics, computer science).
Proficiency with Python programming language including common libraries (Pandas, NumPy, Scikit-learn, Plotly etc) and visualizations
Knowledge and experience with big data tools (SQL, Big Query, Spark, Hadoop)
Experience with dashboarding tools and data visualization
Experience with workflow scheduling (such as Airflow) and data pipelining
Familiarity with building experimentation platforms is a plus
Knowledge of statistics is a plus
2+ years of experience in Data Engineer, Data Warehouse Engineer or Data Scientist roles