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Job Overview
We are looking for a Data Engineer who will be part of our Analytics Practice and will be expected to actively work in a multi-disciplinary fast paced environment. This role requires a broad range of skills and the ability to step into different roles depending on the size and scope of the project; its primary responsibility is the acquisition, transformation, loading and processing of data from a multitude of disparate data sources, including structured and unstructured data for advanced analytics and machine learning in a big data environment.
Responsibilities:
-Engineer a modern data pipeline to collect, organize, and process data from disparate sources. -Performs data management tasks, such as conduct data profiling, assess data quality, and write SQL queries to extract and integrate data - Develop efficient data collection systems and sound strategies for getting quality data from different sources -Consume and analyze data from the data pool to support inference, prediction and recommendation of actionable insights to support business growth. -Design and develop ETL processes using tools and scripting. Troubleshoot and debug ETL processes. Performance tuning and opitimization of the ETL processes. -Provide support to new of existing applications while recommending best practices and leading projects to implement new functionality. - Collaborate in design reviews and code reviews to ensure standards are met. Recommend new standards for visualizations. -Learn and develop new ETL techniques as required to keep up with the contemporary technologies. -Reviews the solution requirements and architecture to ensure selection of appropriate technology, efficient use of resources and integration of multiple systems and technology. -Support presentations to Customers and Partners -Advising on new technology trends and possible adoption to maintain competitive advantage
Experience Needed:
-8+ years of related experience is required. -A BS or Masters degree in Computer Science or related technical discipline is required -ETL experience with data integration to support data marts, extracts and reporting -Experience connecting to varied data sources -Excellent SQL coding experience with performance optimization for data queries. -Understands different data models like normalized, de-normalied, stars, and snowflake models. Worked with transactional, temporarl, time series, and structured and unstructured data. -Experience on Azure Data Factory and Azure Synapse Analytics -Worked in big data environments, cloud data stores, different RDBMS and OLAP solutions. -Experience in cloud-based ETL development processes. -Experience in deployment and maintenance of ETL Jobs. -Is familiar with the principles and practices involved in development and maintenance of software solutions and architectures and in service delivery. -Has strong technical background and remains evergreen with technology and industry developments. At least 3 years of demonstrated success in software engineering, release engineering, and/or configuration management. Highly skilled in scripting languages like PowerShell. Substantial experience in the implementation and exectuion fo CI/CD processes.
Additional Requirements
-Demonstrated ability to have successfully completed multiple, complex technical projects -Prior experience with application delivery using an Onshore/Offshore model -Experience with business processes across multiple Master data domains in a services based company -Demonstrates a rational and organized approach to the tasks undertaken and an awareness of the need to achieve quality. -Demonstrates high standards of professional behavior in dealings with clients, colleagues and staff. -Is able to make sound and far reaching decisions alone on major issues and to take full responsibility for them on a technical basis. -Strong written communication skills. Is effective and persuasive in both written and oral communication. - Experience with gathering end user requirements and writing technical documentation - Time management and multitasking skills to effectively meet deadlines under time-to-market pressure