Senior Data Engineer

About the job

ADSS is a successful retail and institutional brokerage based in Abu Dhabi. The company is currently undergoing a multi-year transformation to become a fast-growing, purpose-led, data-driven enterprise that delivers market-leading personalized content, propositions, and experiences to traders. To reinforce client-centricity in the company structure, technology, and operations are being redesigned alongside the crafting of a high-performance culture underpinned by The ADSS Way. The objective is for ADSS to become a dynamic and innovative, trusted market leader, as measured by feedback from clients, talent, shareholders, and competitors.

As a Senior Data Engineer, you combine solid data engineering and software development skills, data modeling, integration knowledge with your business understanding, and strong analytical skills that enable you to deliver the expected benefits to the business. You will be working with Databricks and Azure services and will have an overview of Engineering activities within the Data team. You will have a point of view on information architecture, and how it can be evolved to support business strategy and functional requirements. You will play a key role in the Data transformation program, interfacing with the Governance, Data Science, Reporting, and Business partnering teams. This is an exciting opportunity to influence the quality of data-based decision-making across the business while working with the latest technologies on a cloud-based environment in a multi-disciplinary team with analysts, data scientists, data engineers, and SMEs.

KEY RESPONSIBILITIES

• Contribute to the development of D&A platform and analytical tools, ensuring easy and standardized access and sharing of data.
• Help evolve, build, and maintain data pipelines for multiple data sources and interface with system owners.
• Ensure consistency of data interfacing with governance and quality data capabilities and tools.
• Ensure the delivery of transformation backlog across source systems, architectural components, and data products.
• Support in understanding the needs of Data Product Teams and other stakeholders.
• Define and deploy the operating model for the engineering team, as well as BAU support models, while coordinating teams and being accountable for its delivery.
• Work closely together with functions, peers, and other relevant roles like data scientists, analysts, or architects across the organization.
• Mentor and coach data engineers, as well as peers in other teams.
• Run the teams based on Agile work principles and support the team in adopting those same principles as well as key tools and processes, such as JIRA, Erwin, Confluence, etc.
• Interface with Waterfall transformation programs and ensure alignment across deliveries and workstreams.

ABOUT YOU

The successful candidate will have excellent communication skills, political understanding, and excellent judgment of priorities. Proven leader with strategic influencing skills, strong decision-making, negotiating and conflict resolution skills, effective relationship building skills both internally and externally. Bachelor/Master’s degree or similar in engineering, computer science or equivalent combination of relevant training and work experience. 10+ years of experience in a similar role in the Financial Services industry. Strong knowledge in designing efficient, robust and automated data infrastructures and platforms, ETL workflows, data warehousing, and Big Data processing. Working experience with Python is preferable. Working experience and understanding of Azure Databricks, Azure Events Hub, Azure Data Lake, Azure Synapse, and Azure SQL is required. Experience in business processing mapping of data and analytics solutions. The ability to manage development and maintenance of Informatica governance and Quality tools (IDQ, EDC & Axon). Experience in driving new data engineering developments (e.g. apply new cutting-edge data engineering methods to improve the performance of data integration, data virtualization use new tools to improve data quality and etc.) Experience with enterprise data modeling principles.