Manager (Data Management)

at  High5hire

KEY RESULTS ACCOUNTABILITIES

Oversee the development and delivery of technical training programs on advanced data governance tools such as Informatica CDGC, data engineering best practices, and AI/ML frameworks to enhance technical proficiency across the organization.
Facilitate cross-functional technical collaboration with the data platform, IT, and engineering teams to ensure governance practices are technically sound and integrated across the organization’s technology landscape.
Promote knowledge sharing between all business units by conducting technical deep dives, sharing technical documentation, and aligning on the use of advanced tools and technologies.
Provide governance oversight and technical support for strategic data and AI initiatives by adopting tools like MLflow for managing the machine learning lifecycle and practices that align with the established governance frameworks and risk management protocols such as the NIST AI RMF (Risk Management Framework) for AI governance.
Lead and oversee technically complex data management projects, ensuring that advanced data engineering, machine learning, and AI methodologies are applied from planning through execution and completion.
Utilize technical insights from implementation experiences to continuously improve and refine data governance frameworks, incorporating the latest in AI-driven data management tools and techniques.
Ensure the effective operation of the 4th IR Lab by integrating technologies like IoT frameworks (e.g., Azure IoT Hub, AWS IoT Core), blockchain platforms (e.g., Hyperledger, Ethereum), and AI-driven automation tools. Build a culture of technical excellence and innovation.
Monitor data quality using automated tools and implement advanced processes to improve data accuracy, reliability, and timeliness, ensuring that decision-making is informed by high-quality data.
Oversee the technical development, implementation, and maintenance of data infrastructure, including modern data lakes, real-time analytics platforms, and cloud-native architectures. Ensure these systems are scalable and secure using IAM (Identity and Access Management) and VPC (Virtual Private Cloud) configurations and are capable of supporting complex data workflows.
Collaborate with data engineers, architects, and scientists across business units to align technical data initiatives, share best practices, and foster a cohesive, technically sound approach to data management and AI deployment using MLOps frameworks.
Advocate for and integrate emerging technologies, such as Generative AI models (e.g., GPT, DALL-E), deep learning frameworks (e.g., Keras, MXNet), and data visualization tools like Tableau and Power BI into the organization’s technology stack. Lead proof-of-concept projects using these technologies and ensure agile methodologies are employed.
Govern the design and implementation of advanced ETL processes utilizing state-of-the-art data integration tools and techniques such as Informatica, ensuring efficient and secure data flow across various platforms.
Ensure compliance with data protection regulations and industry standards by implementing advanced security protocols, encryption techniques, and real-time monitoring tools. Interface with auditors using technical language to explain compensating controls and ensure zero deficiencies.
Drive the development and enforcement of robust data governance frameworks by integrating automated data lineage, metadata management, and AI-driven data quality solutions (in compliance with ISO 8000 standard) using tools such as Collibra, Alation, and Informatica Axon to ensure data integrity, privacy, and security at scale.
Foster a technical community within the organization by leading hands-on sessions in Python, R, and SQL for data analytics, TensorFlow for machine learning, and Apache Airflow for data pipeline management. Support continuous learning through certifications like AWS Certified Data Analytics and Google Professional Data Engineer.
Oversee the development and management of technical roadmaps for data platform enhancements, ensuring alignment with the latest technological advancements such as containerization, microservices architecture, and distributed computing.
Supervise the development and ownership of data and AI strategy with a deep knowledge and focus on advanced technical architectures, including cloud-based platforms such as AWS, Azure, GCP, Apache Hadoop, and Kubernetes, big data technologies, and AI/ML frameworks. Ensure the strategy integrates cutting-edge technologies and aligns with organizational goals.
Supervise the design and implementation of advanced operating models that utilize data engineering, data pipelines, and automated data governance tools. Ensure these models optimize the strategic use of data and analytics while maintaining robust data security measures compliant with ISO/IEC 27001 for data security.
Promote and implement a data-driven culture by leveraging advanced analytics, machine learning models, and AI tools using platforms like Google AI Platform or Azure Machine Learning. Ensure these technologies are embedded into business processes to enhance decision-making and operational efficiency.
Lead the design, deployment, and optimization of data architecture frameworks, ensuring they are technically robust, scalable, and aligned with the organization’s strategic goals and regulatory requirements.
Strictly adhere to the client’s Information Security policies, procedures, and processes.
Strictly follow the Information Security data sharing guidelines for all internal and external data transfers.
Immediately report any Information Security incidents to your line manager and IT support for prompt guidance and resolution.
Actively participate in all assigned Information Security training and awareness sessions to enhance your knowledge and address potential Information Security risks.
Always comply with instructions and guidance provided by your line manager in line with Information Security requirements.

KNOWLEDGE, SKILLS, AND EXPERIENCE

Education
Bachelor’s degree in Data Science, Information Technology, Computer Science, or a related field. A master’s degree is preferred. Certified Data Management Professional (CDMP - Data Quality) Certified Data Management Professional (CDMP - Master). Experience with machine learning and data science tools.
Certified Information Management Professional (CIMP) AWS Certified Solutions Architect – Professional Microsoft Certified: Azure Solutions Architect Expert Certified Artificial Intelligence Practitioner (CAIP) Certified Information Systems Security Professional (CISSP) Project Management Professional (PMP) TOGAF 9 Certified Certified Data Management Professional (CDMP - Data Quality) Certified Data Management Professional (CDMP - Master).
14 years of experience in data management, with a minimum of 3-5 years in a leadership or managerial role.
Skills
Flexible team player who thrives in environments requiring the ability to effectively prioritize and juggle multiple concurrent projects.
Goal-driven leader who maintains a productive climate and confidently motivates, mobilizes, and coaches employees to meet high-performance standards.
Results-driven achiever with exemplary planning and organizational skills, along with a high degree of detail orientation.
Proficiency in data management tools and software (e.g., SQL, ETL tools, data visualization tools).
Strong understanding of data governance, data quality, and data security principles.
Exceptional listener and communicator who effectively conveys information verbally and in writing.
Resourceful team player who excels at building trusting relationships with customers and colleagues.
Highly analytical thinking with demonstrated talent for identifying, scrutinizing, improving, and streamlining complex work processes.
Computer-literate performer with extensive software proficiency covering a wide variety of applications.
Innovative problem-solver who can generate workable solutions and resolve complaints.

Shaping and overseeing the strategic direction of data and AI initiatives across the organization. This role ensures that data governance frameworks, policies, and practices are aligned with business goals, driving the effective use of data as a strategic asset and empowering the client’s agencies and sectors to make informed, data-driven decisions.