Data Engineer

As a Data Engineer, you will be responsible for defining, designing, and building an optimal data pipeline architecture. This includes collecting data from various sources, cleansing it, and organizing it in SQL & NoSQL destinations using ELT & ETL Processes. Your key responsibilities will include: - Building business use case-specific data models for Data Scientists and Data Analysts to derive insights and patterns. - Identifying and implementing internal process improvements such as automation, data delivery optimization, and infrastructure redesign for scalability. - Developing the infrastructure for efficient extraction, transformation, and loading of data from diverse sources using SQL and AWS 'big data' technologies. - Deploying analytical models and tools to generate actionable insights into customer acquisition, operational efficiency, and other key business metrics. - Collaborating with stakeholders to address data-related technical issues and support their data infrastructure requirements. - Designing and creating Executive dashboards and reports to facilitate decision-making and insight generation. - Ensuring data security and separation across on-premises and cloud environments. - Creating data tools for the analytics and data science teams to enhance product optimization. - Working with data and analytics experts to enhance functionality in data systems. - Implementing scheduled data loading processes and managing data pipelines. - Troubleshooting, investigating, and resolving failed data pipelines while preparing Root Cause Analysis (RCA). You should have 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 Feel free to leverage your expertise in these technologies to contribute effectively to our data engineering initiatives. As a Data Engineer, you will be responsible for defining, designing, and building an optimal data pipeline architecture. This includes collecting data from various sources, cleansing it, and organizing it in SQL & NoSQL destinations using ELT & ETL Processes. Your key responsibilities will include: - Building business use case-specific data models for Data Scientists and Data Analysts to derive insights and patterns. - Identifying and implementing internal process improvements such as automation, data delivery optimization, and infrastructure redesign for scalability. - Developing the infrastructure for efficient extraction, transformation, and loading of data from diverse sources using SQL and AWS 'big data' technologies. - Deploying analytical models and tools to generate actionable insights into customer acquisition, operational efficiency, and other key business metrics. - Collaborating with stakeholders to address data-related technical issues and support their data infrastructure requirements. - Designing and creating Executive dashboards and reports to facilitate decision-making and insight generation. - Ensuring data security and separation across on-premises and cloud environments. - Creating data tools for the analytics and data science teams to enhance product optimization. - Working with data and analytics experts to enhance functionality in data systems. - Implementing scheduled data loading processes and managing data pipelines. - Troubleshooting, investigating, and resolving failed data pipelines while preparing Root Cause Analysis (RCA). You should have 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 Feel free to leverage your expertise in these technologies to contribute effectively to our data engineering initiatives.