Educational
Background in programming, databases and/or big data technologies OR
BS/MS in software engineering, computer science, economics or other engineering fields
Responsibility
Partner with Data Architect and Data Integration Engineer to enhance/maintain optimal data pipeline architecture aligned to published standards
Assemble medium, complex data sets that meet functional /non-functional business requirements
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 ‘big data’ technologies
Build analytics 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 Domain leads, and Teams to assist with data-related technical issues and support their data infrastructure needs
Ensure technology footprint adheres to data security policies and procedures related to encryption, obfuscation and role based access
Create data tools for analytics and data scientist team members
Functional Competency
Knowledge of data and analytics framework supporting data lakes, warehouses, marts, reporting, etc
Defining data retention policies, monitoring performance and advising any necessary infrastructure changes based on functional and non-functional requirements
In depth knowledge of data engineering discipline
Extensive experience working with Big Data tools and building data solutions for advanced analytics
Minimum of 5+ years' hands-on experience with a strong data background
Solid programming skills in Java, Python and SQL
Clear hands-on experience with database systems - Hadoop ecosystem, Cloud technologies (e.g. AWS, Azure, Google), in-memory database systems (e.g. HANA, Hazel cast, etc) and other database systems - traditional RDBMS (e.g. Teradata, SQL Server, Oracle), and NoSQL databases (e.g. Cassandra, MongoDB, DynamoDB)
Practical knowledge across data extraction and transformation tools - traditional ETL tools (e.g. Informatica, Ab Initio, Altryx) as well as more recent big data tools
Educational
Background in programming, databases and/or big data technologies OR
BS/MS in software engineering, computer science, economics or other engineering fields
Responsibility
Partner with Data Architect and Data Integration Engineer to enhance/maintain optimal data pipeline architecture aligned to published standards
Assemble medium, complex data sets that meet functional /non-functional business requirements
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 ‘big data’ technologies
Build analytics 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 Domain leads, and Teams to assist with data-related technical issues and support their data infrastructure needs
Ensure technology footprint adheres to data security policies and procedures related to encryption, obfuscation and role based access
Create data tools for analytics and data scientist team members
Functional Competency
Knowledge of data and analytics framework supporting data lakes, warehouses, marts, reporting, etc
Defining data retention policies, monitoring performance and advising any necessary infrastructure changes based on functional and non-functional requirements
In depth knowledge of data engineering discipline
Extensive experience working with Big Data tools and building data solutions for advanced analytics
Minimum of 5+ years' hands-on experience with a strong data background
Solid programming skills in Java, Python and SQL
Clear hands-on experience with database systems - Hadoop ecosystem, Cloud technologies (e.g. AWS, Azure, Google), in-memory database systems (e.g. HANA, Hazel cast, etc) and other database systems - traditional RDBMS (e.g. Teradata, SQL Server, Oracle), and NoSQL databases (e.g. Cassandra, MongoDB, DynamoDB)
Practical knowledge across data extraction and transformation tools - traditional ETL tools (e.g. Informatica, Ab Initio, Altryx) as well as more recent big data tools