Data Engineer
Indexed description
We have an exciting opportunity for an exceptional individual to work supporting one of our clients as a Data Engineer
Responsibilities
- Design robust enterprise data architecture that aligns business objectives with scalable data warehouse solutions ensuring reliable data availability for analytics and operational reporting in a hybrid work environment.
- Develop and optimize complex SQL and Google Big queries and scripts to transform validate and aggregate large data sets improving performance and accuracy for critical business intelligence workloads.
- Configure and manage ETL workflows using IBM Infosphere Datastage to extract transform and load data from diverse source systems into centralized data warehouse structures adhering to quality and compliance standards.
- Implement comprehensive data integration strategies that unify structured and semi structured data across applications enabling consistent data views that support informed decision making and regulatory needs.
- Establish and refine scheduling basics for batch and near real time jobs by defining schedules dependencies and recovery procedures that keep data pipelines predictable and resilient during day shift operations.
- Collaborate with product operations and analytics stakeholders to translate functional requirements into detailed data models and mapping specifications that drive trustworthy reporting assets for the organization and society.
- Review existing data warehouse schemas and data flows to identify inefficiencies then propose and implement architectural enhancements that reduce latency optimize storage usage and improve query response times.
- Define and maintain metadata data lineage and documentation for all data integration processes enabling transparent traceability of data from source to consumption and supporting audit and governance activities.
- Provide technical guidance to development and operations teams on best practices for SQL coding standards Datastage job design and ETL error handling fostering a culture of quality and reliability across projects.
- Coordinate hybrid collaboration routines with onsite and remote colleagues to plan releases validate test results and ensure smooth deployment of new or enhanced data solutions without impacting business continuity.
- Monitor data pipeline executions and warehouse performance dashboards to proactively identify bottlenecks or anomalies initiating corrective actions that protect data freshness and stakeholder confidence.
- Drive continuous improvement initiatives that leverage automation parameterization and reusable components in ETL and integration workflows reducing manual effort and accelerating delivery of new data capabilities.
- Ensure that all data architecture and integration designs observe organizational policies for security privacy and compliance contributing to responsible use of data that benefits customers and the wider community.
- Demonstrate advanced proficiency in SQL and Google Big Queries by crafting efficient queries procedures and functions that support complex transformations and high volume transactional workloads.
- Apply deep understanding of data warehousing concepts such as dimensional modeling star and snowflake schemas and slowly changing dimensions to design resilient analytical repositories.
- Utilize strong ETL and data integration experience to build modular Datastage jobs and frameworks that handle data extraction transformation and loading across multiple environments with minimal defects.
- Show practical knowledge of scheduling basics including job calendars dependency handling and restart mechanisms to maintain consistent pipeline execution across hybrid teams and systems.
- Bring solid experience in IBM Infosphere Datastage configuration tuning and migration activities to ensure high throughput maintainability and scalability of enterprise data integration solutions.
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