We Offer A senior Data Engineer to join our growing team of analytics experts in Finance Data hub In the Product Control IT department in Credit Suisse. The hire will be responsible for expanding and optimizing our data and data pipeline architecture, as well as optimizing data flow and collection for cross functional teams. The ideal candidate is an experienced data pipeline builder and data wrangler who enjoys optimizing data systems and building them from the ground up. The Data Engineer will support our software developers, database architects, data analysts and data scientists on data initiatives and will ensure optimal data delivery architecture is consistent throughout ongoing projects. They must be self-directed and comfortable supporting the data needs of multiple teams, systems and products. The right candidate will be excited by the prospect of optimizing or even re-designing our company's data architecture to support our next generation of products and data initiatives. This is a leadership role, which is also focusing on resource management, driving governance and standards through the wider team . Responsibilities for Data Engineer You will create and maintain optimal data pipeline architecture. You will assemble large, complex data sets that meet functional/non-functional business requirements. Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc. Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL, Sqoop, file based and Kafka. Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics. Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs. Keep our data separated and secure across national boundaries through multiple data centers. Credit Suisse maintains a Working Flexibility Policy, subject to the terms as set forth in the Credit Suisse United States Employment Handbook. You Offer You have advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases. You have experience building and optimizing 'big data' data pipelines, architectures and data sets. Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement. You have strong analytic skills related to working with unstructured datasets. Build processes supporting data transformation, data structures, metadata, dependency and workload management. You have a successful history of manipulating, processing and extracting value from large disconnected datasets. You have working knowledge of message queuing, stream processing, and highly scalable 'big data' data stores. You have strong project management and organizational skills. Other Skills Do you have experience supporting and working with cross-functional teams in a dynamic environment? We are looking for a candidate with 5+ years of experience in a Data Engineer role, who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field. They should also have experience using the following software/tools: Experience with big data tools: Hadoop, Spark, Kafka, etc. Experience with relational SQL and NoSQL databases, including Postgres and Cassandra is optional. Experience with stream-processing systems: Storm, Spark-Streaming, etc. Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc. Your experience with Cloud and Data Science tools such as R and Python is optional. For more information visit Technology Careers.
Data Engineer # 111230
Associated topics: data center, data integration, data integrity, data manager, data management, data quality, data warehousing, database, mongo database, sql