Senior Data Manager

Responsibilities

• Design and execute strategies to source data for sales and marketing campaigns from diverse channels, including internal systems, external vendors, public APIs, web scraping, and scholarly publications.
• Plan, execute, and monitor data acquisition initiatives while adhering to defined timelines, budgets, and quality standards.
• Identify and assess potential data vendors, negotiating agreements to procure external data sources aligned with business requirements.
• Establish and enforce validation protocols to guarantee the accuracy, consistency, completeness, and timeliness of acquired data.
• Partner with data engineers, data scientists, business analysts, and other stakeholders to identify data needs and ensure that acquired data fulfils their requirements.
• Regularly assess the efficiency of data acquisition workflows, addressing bottlenecks and implementing strategies to enhance performance and cost-effectiveness.
• Collaborate with the data engineering team to create, develop, and sustain scalable data pipelines that enable the ingestion, transformation, and integration of data into target systems.
• Examine datasets to derive actionable insights that address stakeholder needs or project objectives.
• Create automated dashboards and reporting solutions that enable stakeholders to seamlessly integrate data into daily operations and maximize insights.
• Supervise daily data processing activities, ensuring the timely delivery of high-quality dashboards, reports, and insights.
• Lead data-related projects, maintaining alignment with defined objectives, budgets, and schedules.
• Define and implement a robust data governance framework that aligns with the organizations strategic goals and operational needs.

Skills/behaviour Required:

• Strong track record in Business intelligence, data analytics or data engineering, (ideally 5 years), with strong proficiency in SQL, SQL Server, Power Bi etc
• Advanced data manipulation and analysis skills, particularly with SQL and data modelling techniques.
• Strong knowledge of Excel is essential.
• Strong background in designing data structures, implementing controls, and applying statistical and visualisation techniques.