Senior Application Engineer

SKILLS:

. Minimum 5 years of hands-on experience in data and application support.
. Strong experience and knowledge in Python and SQL.
. Good written and verbal communication skills.
. Agile, fast learner and able to adapt to changes.

Key Responsibilities:

Planning and Design
- Work closely with business analysts, stakeholders, users, and technology teams to understand business requirements, user journeys, functional specifications, and data requirements.
- Design, build, test, and maintain scalable data pipelines, data models, and data integration solutions that support business and operational needs.
- Manage system enhancements, bug fixes, production issues, and change requests, ensuring changes are properly assessed, tested, documented, and aligned with business needs.

Development
- Translate functional and data requirements into robust technical designs, ensuring solutions are reliable, maintainable, and aligned with the defined user journeys.
- Source, ingest, transform, and curate data from multiple internal and external systems, ensuring data is fit for downstream consumption and analytics.
- Work closely with business analysts to validate data logic, business rules, data mappings, and transformation requirements.
- Conduct data profiling, reconciliation, testing, and root-cause analysis to ensure data quality, consistency, completeness, and accuracy.

Documentation and governance
- Develop and maintain technical documentation, including data pipeline designs, data flow diagrams, data mappings, data models, interface specifications, and operational runbooks.
- Support data governance and data management initiatives by implementing required controls, standards, and processes within data solutions.
- Ensure data engineering solutions comply with internal technology, security, risk, and data governance standards.

BAU activities
- Perform BAU monitoring, incident investigation, root-cause analysis, remediation, and production support for data platforms and pipelines.
- Monitor pipeline, platform, and system performance, and proactively identify opportunities for optimization, automation, and resilience improvements.