Data Integrations Engineer

BASIC PURPOSE :

The Data Integrations Engineer is a vital role within the Enterprise Data Management (EDM) team, primarily responsible for the development, implementation, and maintenance of data integration solutions. This role aims to facilitate efficient data interchange between various internal and external systems, ensuring data accuracy, timeliness, and accessibility to support the organization's decision-making and operational efficiency.

POSITION RESPONSIBILITIES :

Develop, Implement, and Support Data Integration Solutions : Design, build, and maintain robust data integration pipelines using batch, API, and data streaming technologies like Python, dbt, NiFi, Kafka, Striim, Fivetran, Informatica, Matillion, and SSIS. Ensure these solutions meet business requirements and integrate seamlessly with existing systems. Provide 24 / 7 on-call support in rotating schedules.

Data Warehouse Support : Work closely with the Data Warehouse team to implement data ingestion solutions ensuring efficient and reliable data availability from source systems.

Performance Monitoring and Optimization : Work closely with Ops and DevOps teams to improve the performance of data integrations, identify bottlenecks, and implement optimizations to ensure efficient data processing and movement.

Collaboration and Cross-Functional Support : Collaborate with Data DevOps & Ops teams to support the daily operation of the data integration platform. Work closely with the direct and indirect EDM leadership roles like Product and QA to align data strategies with organizational goals.

Data Quality and Compliance : Ensure the highest standards of data quality and compliance with relevant data governance and privacy policies.

Technical Innovation and Continuous Improvement : Stay abreast of emerging technologies and industry trends in data integration. Recommend and implement improvements to enhance system performance and data integration capabilities.

Perform other job-related functions as assigned.

KNOWLEDGE AND EXPERIENCE :

EDUCATION : Bachelor's degree (B. S.) Degree in Computer Science, Engineering or related field of study and 2 to 4 years related experience and / or training; or equivalent combination of education and experience.

EXPERIENCE : Minimum 5-years relational database experience in data integrations required including backend integration using batch ETLs, API, and data streaming technologies like Kafka, Striim, Fivetran, Informatica, Matillion, and SSIS. Strong background in database programming and system integrations. Experience with relational database technologies, T-SQL, PL / SQL and thorough experience with SQL Server or Oracle RDBMS platform. Experience troubleshooting OS and network issues impacting data integration processes and platforms.

KNOWLEDGE & SKILLS :

Mid-to-advanced level in developing and implementing system integrations using database platforms (e.g., SQL Server, Oracle, Snowflake, Redshift, Hadoop). Requires experience in object oriented programming languages such as C# and Java, and scripting languages like Python and PowerShell. Requires experience with technologies and standards such as Microsoft Message Queue, Kafka, IBM MQ, .NET, JSON, ASP.NET. Familiarity with Agile SDLC. Ability to read, analyze and interpret common scientific and technical journals, financial reports, and legal documents. Ability to respond to common inquiries or complaints from customers, regulatory agencies, or members of the business community. Ability to effectively present information to cross-functional teams and stakeholders. Ability to apply advanced mathematical concepts such as exponents, logarithms, quadratic equations, and permutations. Ability to apply mathematical operations to such tasks as frequency distribution, determination of test reliability and validity, analysis of variance, correlation techniques, sampling theory, and factor analysis. Ability to apply principles of logical or scientific thinking to a wide range of intellectual and practical problems. Ability to deal with nonverbal symbolism (formulas, scientific equations, graphs, musical notes, etc.,) in its most difficult phases. Ability to deal with a variety of abstract and concrete variables.

Proven track record of; working in several organizational models such as matrixed resources. Requires expertise in data analytics skill sets including data mining, regression analysis and data extraction. Excellence in a variety of competencies including teamwork / collaboration, analytical thinking, communication and influencing skills, and technical expertise. Excellent teamwork and interpersonal skills. Must be able to communicate effectively with business users, members of IT, executive management and outside vendors.