Data Engineering Asc

Description:The Data Engineer delivers full-stack data solutions across the entire data processing pipeline. This relies on systems engineering principles to design and implement solutions that span the data lifecycle to: collect, ingest, process, store, persist, access, and deliver data at scale and at speed. It includes knowledge of local, distributed, and cloud-based technologies; data virtualization and smart caching; and all security and authentication mechanisms required to protect the data.

- Build data pipelines that clean, transform, and aggregate unorganized data into databases or data sources that are ready for analysis
- Design and implement data solutions by defining functional capabilities, security, back-up, and recovery specifications
- Work through all stages of a data solution lifecycle, e.g., analyze / profile data, create conceptual, logical and physical data model designs, architect and design ETL, reporting and analytics
- Maintain data systems performance by identifying and resolving production and application development problems; calculating optimum values for parameters; evaluating, integrating, and installing new releases
- Define standards, best practices, and certification processes for data objects
- Conduct performance tuning and optimization of data processing and storage
- Write complex queries that can scale to meet requirements
- Verification/validation that data solutions and/or system performance meets requirements
- Document data definitions, dictionaries, and architectures
- Define how data will be collected, what data will be needed, constraints to be considered
Basic Qualifications:
- Degree in Computer Science, Systems Engineering, or related field
- Systems Engineering expertise to include systems design, requirements, analysis, and management of complex systems over their lifecycles
- Experienced in design or development of enterprise data solutions, applications, and integrations
- Knowledge of modern enterprise data architectures, design patterns, and data toolsets and the ability to apply them
- Proficiency in data modeling techniques and understanding of normalization
- Has software engineering experience
- Strong problem solving, conceptualization, and communication skills
Desired Skills:
- Data APIs
- Database systems (SQL and NO SQL)
- Distributed data systems (e.g., Hadoop, HBase, Cassandra, Spark)
- Data modeling
- Extraction, Transformation and Load (ETL) tools
- Data warehousing solutions (Time phasing, Dimensional modeling, Snapshot)
- Database architecture
- Languages: Java Script, SQL, Hive, Pig, Python, XML, Java, Shell Scripting

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