Data Engineer

• 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