About the job
The Data Engineer will play a pivotal role in the design, development, and maintenance of our organization's enterprise data platform, crucial for driving scalable operations, accurate reporting, and informed decision-making. As the technical lead on data integration across essential systems, you will establish robust pipelines, standardized data models, and quality assurance measures to ensure a single, reliable source of truth.
As our organization expands, this position will be vital in modernizing the data landscape, minimizing manual reporting efforts, and facilitating self-service analytics with proper governance. Within the first year, you will establish foundational data pipelines, aid in platform modernization initiatives, and enhance access to consistent and reliable data across the organization.
Key Duties and Responsibilities:
Data Engineering & Architecture
- Design and maintain scalable data pipelines that integrate enterprise systems.
- Build and oversee centralized data warehouse structures.
- Develop ETL/ELT processes to automate data ingestion and transformation workflows.
- Create standardized, reusable data models that align with business definitions.
Data Quality & Reliability
- Implement automated validation and monitoring processes to ensure data accuracy.
- Identify and resolve discrepancies across systems and reports.
- Establish data lineage while improving dataset performance and reliability.
Analytics Enablement
- Collaborate with stakeholders to deliver optimized, certified datasets.
- Support self-service analytics while adhering to governance standards.
- Reduce dependency on manual reporting and ad hoc data requests.
Platform Integration & Collaboration
- Assist in system integrations across operational and internally developed platforms.
- Provide insights into platform design and data structure standardization.
- Work closely with technical and business teams to align data solutions with organizational needs.
Data Governance
- Support data governance standards, policies, and best practices.
- Contribute to data definitions, documentation, and cataloging initiatives.
- Promote consistent and accurate data usage throughout the organization.
