Data Engineer
Skills
About This Role
Overview
- · Design, build, and maintain robust, scalable, and secure ETL/ELT pipelines to ingest structured and unstructured data from internal, external, and third‑party sources.
- · Support the integration, population, and optimization of the organization Data Lakehouse and data platforms in alignment with the approved enterprise data architecture.
- · Ensure data pipelines support both batch and real‑time processing requirements, and are optimized for performance, reliability, and high availability.
- · Collaborate closely with the Data Architect to ensure pipeline designs comply with architectural standards, integration patterns, APIs, and interoperability blueprints.
- · Implement and embed data quality checks, validation rules, metadata capture, lineage tracking, and data classification workflows to support governance and trusted analytics.
- · Monitor the performance, progress, and health of data pipelines and platforms, proactively identifying bottlenecks, failures, and optimization opportunities to improve efficiency and productivity.
- · Enable reliable data provisioning for dashboards, AI/ML models, reporting solutions, and self‑service analytics tools used across the organization.
- · Coordinate with business units, analysts, and data scientists to understand requirements and translate them into actionable data engineering tasks and delivery plans.
- · Act as a liaison between the organization's teams and vendors for data platform–related activities, ensuring alignment on priorities, timelines, and technical deliverables.
- · Document technical designs, configurations, workflows, and building procedures, and support agile project delivery, onboarding of new data sources, and continuous platform improvements.
- · Undertake all duties and responsibilities reasonably required and aligned with the data management & governance.
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