Data Integration Engineer

.**Responsibilities**- Data pipeline creation:Create data pipelines to onboard a variety of new datasets using your technical skillsetImprove existing processes to reduce data pipeline error counts and increase automation of data onboarding processes- Data curation:Create data quality scripts to identify and remediate poor quality dataWrangle / manipulate data to make datasets fit for usage by client's front office teamsCurate disparate datasets into well-defined and cross-mapped datasets**Requirements**:- Sound SQL skillset for data interrogation and manipulation- Expert Python skillset for data analysis and automation - experience pandas, numpy, etc- Code management tools and experience of working from a checked-in code base- Industry knowledge / experience- Experience in the financial market (nice to have)- Knowledge of Apache Spark- Experience working with Microsoft Azure- Data engineering / data wrangling / data cleaning / data architecture experience- Experience with industry vendors e.G., Bloomberg, Refinitiv, IHS Markit- Experience in Agile projects, working knowledge of Jira and Confluence- The right attitude to navigate uncertainty and / or complexity whilst retaining a positive outlook- Clear and concise communication skills, and the ability to transfer knowledge efficiently- Attention to detail and strive for continual improvement- Ability to form sound, honest, and mutually respectful relationships with the key client, visiting their London office as required- Data Lake / Delta Lake / Data Mesh methodologies. Inmon vs Kimball, Staging areas, SCD and other dimension types, advanced and hybrid-data modeling approaches: Data Vault, NoSQL structures- Advanced knowledge of Data Integration tools (Azure Data Factory, AWS Glue, GCP Dataflow, Talend, Informatica, Pentaho, Apache NiFi, KNIME, SSIS, etc