Job Description
Define, Design, and Build an optimal data pipeline architecture to collect data from a variety of sources, cleanse, and organize data in SQL & NoSQL destinations (ELT & ETL Processes).
Define and Build business use case-specific data models that can be consumed by Data Scientists and Data Analysts to conduct discovery and drive business insights and patterns.
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies.
Build and deploy analytical models and tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
Work with stakeholders including the Executive, Product, Data, and Design teams to assist with data-related technical issues and support their data infrastructure needs.
Define, Design, and Build Executive dashboards and reports catalogs to serve decision-making and insight generation needs.
Provide inputs to help keep data separated and secure across data centers – on-prem and private and public cloud environments.
Create data tools for analytics and data science team members that assist them in building and optimizing our product into an innovative industry leader.
Work with data and analytics experts to strive for greater functionality in our data systems.
Implement scheduled data load process and maintain and manage the data pipelines.
Troubleshoot, investigate, and fix failed data pipelines and prepare RCA.
Experience with a mix of the following Data Engineering Technologies
Python, Spark, Snowflake, Databricks, Hadoop (CDH), Hive, Sqoop, oozie
SQL – Postgres, MySQL, MS SQL Server
Azure – ADF, Synapse Analytics, SQL Server, ADLS G2
AWS – Redshift, EMR cluster, S3
Experience with a mix of the following Data Analytics and Visualization toolsets
SQL, PowerBI, Tableau, Looker, Python, R
Python libraries -- Pandas, Scikit-learn, Seaborn, Matplotlib, TF, Stat-Models, PySpark, Spark-SQL, R, SAS, Julia, SPSS,
Azure – Synapse Analytics, Azure ML studio, Azure Auto ML
