The primary task is to drive and transform the data capabilities and enable a data-driven culture across the group and therefore work with other architects and platform teams to ensure data is managed as an asset in a centralized, standardized, and consistent manner in order to maintain consistency and quality, using mature technologies and emerging data practices.
This role requires an understanding of various data engineering and processing related technologies and choices, has a deep understanding of both SQL/No-SQL technologies, distributed computing framework, and techniques to make right tools and technology choices.

KEY QUALIFICATIONS:

• Education: streams, information systems management or related field. Solution and Architecture certifications such as TOGAF or other.
• Experiences: More than 12 years of experience in information technology where mostly spent in data architecture/engineering or technology solutions definitions and implementations. More than 8 years of experience in leadership, technology road-mapping and exposed to software development. Extensive experience in banking and financial services domain.
• Knowledge & Skills: Expertise in Data Architecture, Data Strategy and Roadmap for large and complex organization and systems and implemented large scale end-to-end Data Management & Analytics solutions. Experience in transforming traditional Data Warehousing approaches to Big Data based approaches and proven track record of managing risks and data security.
• Expertise with Dimensional modeling techniques, Star & Snowflake schemas, modeling slowly changing dimensions and role-playing dimensions, dimensional hierarchies, and data classification. Experiences in cloud native principals, designs, and deployments. Extensive experience working with and enhancing Continuous Integration (CI) and Continuous Development (CD) environments. Expertise in Data Quality, Data Profiling, Data Governance, Data Security, Metadata Management, and Data Archival. Data Migration strategies using appropriate tools. Drive delivery in a matrixed environment working with various internal IT partners. Demonstrated ability to work in a fast paced and changing environment with short deadlines, interruptions, and multiple tasks/projects occurring simultaneously. Must be able to work independently and have skills in planning, strategy, estimation, scheduling. Strong problem solving, influencing, communication, and presentation skills, self-starter. Experience with data processing frameworks and platforms (Kafka, Beam, Flink, Hadoop, Hive, Spark etc.). Hands-on experience with related/complementary open-source software platforms and languages (e.g., Java, Linux, Python, GIT, Jenkins, MLOps). Hands-on experience with BI tools and reporting software (e.g., MS PowerBI and Cognos Reporting)

• Develop and deliver long-term strategic goals for data engineering: Develop data architecture/engineering vision and standards in conjunction with business users and stewards. Outline short-term incremental solutions to achieve long-term objectives and an overall data management roadmap. Create data exchange standards to ensure reusability and a decoupled architecture. Create standards for data archival and purging.
• Maintain an agile, resilient, and secured data architecture/engineering landscape: Identify and drive the implementation of secured data solutions. Develop data access matrix and ensure right information reaches the right people through secured channel. Develop strategy to align with external regulatory requirements.
• Establish methods and procedures for tracking data quality, completeness, redundancy, and compliance: Identify and document critical data elements across source system of records. Define and document the data quality rules and standards. Assess and define data governance and stewardship maturity roadmap.
• Establish standards to document and communicate data flow for enterprise data warehouse and data lack: Work with data engineers and source system experts to define the standards and principles for capturing lineage and data flow. Define data exception handling processes. Define standards for data pipeline scheduling and monitoring.
• Coach and mentor, the data engineering community: Provide technical recommendations and engage with data engineers and BI leads throughout the solutioning and implementation lifecycle.
• Recommend effective solutions to develop high performant and scalable data pipelines.
• Work with source system expertise to understand the data domains and source to target mapping. Build and maintain canonical data model to standardize data exchange between systems and with various architects to enforce the same.