Azure Data Engineer

Job Description

Key responsibilities Development Hands on, sleeves up development and delivery expected as a matter of course. Delivery Ensure project goals are achieved on time in alignment with the stakeholders’ expectation. Ability to work on complex projects and in a distributed environment. Escalate when necessary and in a timely manner. Work in close collaboration with other team members in the Enterprise Data & Analytics Platform team, to ensure Development/Delivery aspects are well represented in the project’s requirements and deliverables. Methodology Incorporate agile ways of working into the delivery process utilising DABL (Discovery, Alpha, Beta, Launch) Individuals will work as part of product-centric delivery team(s) that will focus on delivering value independently while fully embracing integrated DevOps approaches. Ownership Take ownership for the delivery/development projects and help steer until completion Governance Maintain governance that allows projects and stakeholders to manage overall project performance and manage programme risks within the global nature of some of the programmes. Forward looking: Remain flexible towards technology approaches to ensure we are taking advantage of new technologies. Keep abreast of industry developments in analytics and be able to interpret how these would impact?services and present new opportunities. Quality, Risk & Compliance: Ensure all risk and issues associated with owned projects are recorded and managed in the appropriate Risk & Issue logs in a timely manner. Ensure all Risks and Issues have clear action/mitigation/contingency plans defined, with named action owners and timelines for completion. Technical Architecture Be conversant with technical architecture to contribute to design discussions in partnership with the Delivery/Development Lead and dedicated Analytics & Data Architect. Qualifications and skills Essential MS/BS degree in Computer Science, Engineering, Data Science or equivalent experience, with preference on experience and proven track record. Ideal candidate would have an impressive hands-on work history in an advanced, recognized, and innovative environment. 5 to 8 Years Data engineering experience and seasoned coder in the relevant languages: Python, SQL, Scala, etc. Experience with the Azure data and analytics stack: Databricks, Data Factory, SQL DW, Cosmos DB, Power BI, Power Apps, etc. Experience integrating and supporting a variety of enterprise data tools: Ataccama, Talend, Collibra, Snowflake, StreamSets, etc. Fully conversant with big-data processing approaches and “schema-on-read” methodologies. Preference for deep understanding of Spark, Databricks and Delta Lake, and applying them to solve data science and machine learning business problems. Experience with Agile delivery frameworks and tools: SAFe, Jira, Confluence, Azure DevOps, etc. Experience with visualization tools and their application: developing reports, dashboards and KPI scorecards. Familiar deploying enterprise analytics solutions at scale with applicable services: administration, qualification, and user access provisioning. Experience articulating business value of analytics projects and progressing solutions from MVP to scaled-up production solutions. Ability to work in close partnership with groups across the IT organization (security, compliance, infrastructure, etc.) and business stakeholders in the commercial organizations. Ability to develop and maintain productive working relationships with suppliers and specialist technology providers to assemble and maintain a distinctive and flexible mix of capabilities against future requirements. Ideal candidate possesses great communication skills and the ability to communicate inherently complicated technical concepts to non-technical stakeholders of all levels.