Data Engineer – Mastery

Job Description

Note: More details below for DE, Investments Domain role. Domain Key words : Capital Markets, Investment Banking Technical Key words: Informatica, batch/shell scripting, data warehousing , SQL, ETL, Python (basic to intermediate) Objectives of the role Design, build and maintain complex ELT jobs that deliver business value Translate high-level business requirements into technical specs Ingest data from disparate sources into the data lake and data warehouse Cleanse and enrich data and apply adequate data quality controls Provide insight and direction to guide the future development of organization’s data platform Develop re-usable tools to help streamline the delivery of new projects Collaborate closely with other developers and provide mentorship Evaluate and recommend tools, technologies, processes and reference architectures Work in an Agile development environment, attending daily stand-up meetings and delivering incremental improvements Basic Qualifications Bachelor’s degree in computer science, engineering or a related field Data: 8+ years of experience with data analytics and warehousing in Investment & Finance Domain SQL: Deep knowledge of SQL and query optimization ELT: Good understanding of ELT methodologies and tools Troubleshooting: Experience with troubleshooting and root cause analysis to determine and remediate potential issues Communication: Excellent communication, problem solving and organizational and analytical skills Able to work independently and to provide leadership to small teams of developers 3+ years of coding and scripting (Python, Java, Scala) and design experience. 3+ years of experience with Spark framework. Experience with Vertica or any Columnar Databases. Strong data integrity, analytical and multitasking skills. Preferred Qualifications Master’s degree in computer science or engineering or a related field Cloud: Experience working in a cloud environment (e.g. AWS) Python: Hands on experience developing with Python Advanced Data Processing: Experience using data processing technologies such as Apache Spark or Kafka Workflow: Good knowledge of orchestration and scheduling tools (e.g. Apache Airflow) Reporting: Experience with data reporting (e.g. Microstrategy, Tableau, Looker) and data cataloging tools (e.g. Alation) Show more Show less