Data Analysis and Modeling. Models and communicates transactional, analytical and other non-relational database designs for business applications, from data analysis through conceptual, logical and physical models targeting leading RDMS’ like SQL Server or Oracle. Understands best-practice model selection, design approach and modeling techniques for specific business functional and operational needs, and considering required development “agility.”
Database Development. Develops and optimizes database code and query execution plans, with expertise in query languages like T-SQL, PL-SQL and NoSQL languages and APIs, and considering best practices, standards, and appropriate use of RDMS and NoSQL-specific capabilities and constructs.
Data Integration (ETL). Designs and develops robust ETL processes using tools like SQL Server Integration Services (SSIS) or IBM’s DataStage, sourcing from a broad set of disparate data sources. Applies best data integration patterns as fit-for-purpose and aligned with business needs and architectural guidelines.
Data Services. Understands data service design and development patterns using protocols like REST and SOAP on the Java stack, and based on deep understanding of underlying data models, platform capabilities and data usage.
General Engineering. Combines database and data engineering skillsets with strong understanding of Web UI (e.g., HTML, CSS, JavaScript), traditional Java middleware (e.g., JBoss), and modern MV* frameworks (e.g., AngularJS, Backbone.js, Ember.js), to support “full-stack” engineering teams.
Analytics. Understands semantic models for data analytics using tools like SQL Server Analysis Services (SSAS), with experience guiding best-practice use of reporting vs. analytics tools.
Database Platforms. Plans, tunes, optimizes and supports monitoring of large-scale relational and non-relational databases across platforms like SQL Server and Oracle, considering things like performance SLAs, user concurrency and usage patterns, transaction volumes and growth patterns, DBMS configuration requirements, and security.
Data Quality and Security. Understands and drives data quality and security standards and practices, with a demonstrated understanding of data classifications and confidentiality/integrity/availability requirements, common data security threats and vulnerabilities, environment risk impacts and tolerance, and practical mitigation strategies including the appropriate use of at-rest/in-transit encryption and other controls.
Advanced Technologies. Stays current with modern technologies sets and patterns like “big data” on Hadoop/Spark, NoSQL and supporting models on CouchDB, MongoDB, Cassandra or DynamoDB, and data virtualization using technologies like Denodo. Ultimately understands where to proactively introduce these tools and patterns as best fit-for-purpose and architectural alignment.
Data Engineers have to possess and demonstrate systems and critical thinking, and to leverage these skills in a collaborative team based environment.