Data Engineer

Summary

The Data Engineer will play a key role in designing and delivering robust data solutions that enable AI and advanced analytics across the organization. This role owns the end‑to‑end data lifecycle: from requirement gathering and data mapping to implementing scalable pipelines, managing metadata, and creating high‑level reporting visualizations.

A primary focus of this role is to ingest data from disparate engineering and business tools into a single, curated, well‑structured data lake and data catalogue. The engineer will transform raw, fragmented data into curated, high‑quality datasets from multiple source platforms, so that data scientists can easily discover datasets in the catalogue and export them in table format for modeling and analytics.

Detailed Description

• Requirement Management: Facilitate deep‑dive sessions with business and technology leaders to gather and manage technical requirements, ensuring data initiatives align with strategic organizational goals.
• Data Ingestion & Integration: Design and implement connectors and ingestion jobs that pull data from multiple tools (e.g., Azure DevOps, Jira, test systems, logs) into the central data lake.
• Data Curation, Metadata Management & Governance: Lead efforts to clean and curate complex datasets. Develop and maintain data catalogues, metadata, and data mapping documentation to ensure transparency, lineage, and trust across the ecosystem.
• End‑to‑End Pipeline & ETL Development: Architect, implement, and optimize scalable ETL/ELT processes, moving data from diverse sources into enterprise‑grade data lake and warehouse environments.
• Automation & Python Scripting: Leverage Python to automate data validation, monitoring, and repetitive workflows, improving the efficiency and reliability of the data ecosystem.
• Advanced Analytics & Visualization: Design and implement dashboards and reports using platforms like Power BI or Tableau to provide actionable insights to stakeholders at all levels.
• Data Integrity & Security: Implement rigorous data quality checks, monitoring, and security controls to maintain accuracy, compliance, and “gold‑standard” data health.
• Cross‑Functional Collaboration: Partner with data scientists and other teams to understand their access patterns, ensure datasets are documented and queryable, and streamline export of curated tables for downstream ML and analytics.

Knowledge/Skills/Competencies

Physical Demands

• Duties of this position are performed in a normal office environment.
• Duties may require extended periods of sitting and sustained visual concentration on a computer monitor or on numbers and other detailed data. 
• Repetitive manual movements (e.g., data entry, using a computer mouse, using a calculator, etc.) are frequently required.
• Occasional travel may be required.

Typical Experience

• 6 to 10 years

Typical Education

Bachelor degree or consideration of an equivalent combination of education and experience.

Educational Requirements may vary by Geography

Notes

This job description is not intended to be an exhaustive list of all duties and responsibilities of the position. Employees are held accountable for all duties of the job. Job duties and the % of time identified for any function are subject to change at any time.