Creation of data products for all consumers – business users, analysts, and modelers. Explore and understand data sets.
Visualize the data set; determine whether the data set has enough information to answer the question that the business is asking.
Work with IT support to create ETL / ELT interfaces to the data lake and create and visualize the data and data products on the data lake.
Building data pipelines, integrating and scheduling ETL jobs using CI/CD framework.
Implement required data transformation in the data lake.
Configure required security and data masking to a data set
Support testing of data acquisition, data set correlation, and / or model development. Investigate and resolve interface issues
Work with IT to harden and productionize the model, model interfaces, and business procedures.
Requirements:
Master’s/ Bachelor’s Degree in one of the following: Engineering, Statistical Analytics, Data Science, or Actuarial Science.
At least 5 years of relevant work experience in implementing data and analytics projects.
The resources must have domain technical experience in delivering data engineering solutions using data lake technology
Experience with the following: Hadoop (CDH), relational databases and SQL, ETL development, spark, data validation and testing (Data Warehousing, ETL/ELT to the Data Lake, Using the Data Lake for data analysis (Hadoop tools – Hive, Impala, Pig, Sqoop, Hue, Kafka, etc., Spark, Python, R, java, Docker, Dakota).
Working experience in Shell Scripts, Oozie workflows, scheduling tools (Stone branch or CA7).
Knowledge of Cloud platform implementation (Azure or Amazon). Knowledge of data visualization tools is a plus (Tableau on multiple platforms along with Python visualization in the Data Lake using Pandas and bokeh packages)
Excellent written, verbal and interpersonal skills, a must as there will be significant collaboration with the business and IT
Experience with collaborative development workflows (e.g., Microsoft DevOps Tools).
Preferred Experience:
Work experience with the Marketing function leaders to define marketing analytics strategy, provide data analytics support for the design, execution and measurement of campaigns
Improve data and analytics footprint in the Marketing function, Experience with measurements for campaign effectiveness. Understanding of marketing media and campaigns.