Qualifications

• 6-8 years' experience in data integration, data warehouse, and/or big data development, incorporating data architecture patterns and standards, as well as data integration design principles, working on an agile team, Required
• Bachelor's degree in Computer Science or related field, Required
• Knowledge of various architectures, patterns, and protocols, such as data virtualization, UDM, CQRS, CDM, data lake, data warehouse, multi-cloud
• Hands-on experience with data fabric pattern or knowledge of hybrid integration platforms and application integration (APIs, RPC, SOA, messaging)
• Expert SQL knowledge, knowledge of NoSQL types, time-series, ledger, other immerging types
• Expert level data integration skills, preferably utilizing Azure Data Factory, Azure Databricks, or an OSS option such as NiFi
• Expertise in at least one big data programming language
• Understand agile data modeling, lineage, other metadata management
• Knowledge of data ingestion techniques, including streaming, synchronization, real-time, near real-time and mini-batch
• Knowledge of data management practices, including master data management, data governance and data product management
• Conversant in basic statistical and machine learning concepts, to collaborate effectively with data scientists and analysts
• Adaptability, learn and adopt new technology
• Advanced analytical and problem-solving skills

Responsibilities

• Using a variety of integration tools and patterns, the Senior Data Integration Engineer delivers specialized software that ingests, transports, and stores business data and uses technology to organize related information, implementing common data models, and supplying data to all types of data consumers
• The Senior Data Integration Engineer selects the tool or pattern best suited to the problem at hand, conducts optimization and looks for alternatives
• Develop and manage the integration of data -- orchestration, ingestion, storage, format, transport, pipelines, and provisioning
• Influence the selection of tools and technology
• Create and modify functions, programs, routines and procedures to export, transform and load data
• Advice team to re-use, reduce size of code-base
• Develop logical and physical data models
• Work with IT, business and data management teams to understand data needs and develop specialized solutions
• Collaborate with application integration and development teams on the selection and development of application data structures, storage, and integration in accordance with enterprise architectural standards
• Design for data quality, flexibility, and supportability
• Protect the organization and its assets with a strong consistent security awareness
• Optimize data integrations, balancing cloud and maintenance costs with performance
• Track industry changes and recommend improvements in data integration and architecture
• Use DevOps principles and agile development practices to increase quality and reliability of software delivery
• Build automated tests and test datasets for repeatable software quality assurance testing
• Maintain reference documentation