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Job Title - Data Engineer
Location - Atlanta, GA (Remote till Covid)
Job Description:
Key Responsibilities:
Build pipelines to manage audience data for personalised marketing campaigns, integrate
audience data from various sources to build one view of the customer
Perform experimentation at scale by tagging audiences based on complex hierarchy of
customer, product, and regional factors
Responsible for development of the workflow orchestration & ETL pipelines within marketing's
analytics and data science platforms. You will ensure that the data pipeline infrastructure meets
the analysis, reporting, and data science needs of the marketing organization.
Implement continued design, development and optimization of the marketing data pipelines &
data prep infrastructure built on cutting-edge cloud technologies.
Embrace an active team role to help design, implement, and launch efficient and reliable data
pipelines moving data across several platforms including Data Warehouse, online caches, and
real-time systems.
Create data architecture that is flexible, scalable, and consistent for cross-functional use, and
aligned to stakeholder business requirements.
Deploy workflow orchestration and demonstrate expertise in data modeling, ETL development,
and data warehousing
Validate Data Engineering business data elements, organizational and business intelligence
architecture designs for engineering functional areas from Dashboards, Data Lakes, Data
Operations, ML - AI, and upstream/downstream intake and output processes
Knowledge
Qualification BA/BS Degree in Computer Science, any Engineering discipline,
Statistics, Information Systems or another quantitative field.
Experience
5+ years of industry experience in data engineering, data science,
or related field with a track record of manipulating, processing,
and extracting value from large datasets
Work experience with developers and business areas to design,
configure, deploy, and maintain custom ETL Infrastructure to
support project initiatives
Experience in interacting with multiple teams, client stakeholders
and able to fix the issues in a shorter span of time
Skills
Technical/Domain Experience building and managing data pipelines and
repositories in cloud environments such as Google Cloud,
Microsoft Azure or AWS
Experience in Airflow is a must
Experience extracting/cleansing data and generating insights
from large transactional data sets using Spark SQL, SQL, Python,
and PySpark on cloud
Experience with optimizing Spark pipelines on Dataproc,
Databricks or similar technologies.
Competencies
Behavioural Must have exceptional and effective communication,
organization, and time management skills
Must be pro-active and determined (Initiates action, Analytical
and problem solver)
