Responsibilities:
- Technical Leadership: Act as a subject-matter expert for data engineering practices, providing technical guidance and hands-on support to peers and junior engineers. Champion best practices for scalable, efficient, and secure data systems.
- Data Infrastructure Strategy: Contribute to the strategic design and implementation of our data infrastructure to support current and future organizational needs. Research and recommend new tools, technologies, and methodologies to optimize processes.
- Data Pipeline Development: Design, develop, and maintain robust, scalable data pipelines for data ingestion, transformation, and storage. Ensure the quality, reliability, and integrity of data throughout the pipeline lifecycle.
- Data Modeling and Integration: Collaborate with data scientists, analysts, and application teams to design and implement data models that support analytics, reporting, and business intelligence needs. Integrate data from diverse internal and external sources for seamless accessibility.
- Performance Optimization: Analyze and address bottlenecks, inefficiencies, and processing issues in data systems. Conduct capacity planning and implement optimizations to improve performance and reliability.
- Data Security and Governance: Work with stakeholders to establish and enforce data security and governance policies. Implement appropriate access controls, data privacy measures, and compliance with regulatory standards.
- Cross-Functional Collaboration: Partner with teams across the organization to align data engineering efforts with business objectives. Clearly communicate technical concepts to stakeholders with varying technical backgrounds.
- Mentorship: Provide mentorship and technical support to other engineers on the team, fostering a culture of continuous learning and technical excellence.
Qualifications:
- Bachelor’s or Master’s degree in computer science, data science, or a related field. Advanced certifications in data engineering or relevant domains are a plus.
- Proven experience (5+ years) in data engineering, with hands-on expertise in data integration, data modeling, and ETL processes.
- Proficiency in programming languages such as Python, SQL, and Javascript/Typescript.
- Strong understanding of data warehousing concepts, data architecture, and cloud-based platforms (e.g., Dagster, AWS, Snowflake, dbt).
- Experience with data governance, security, and compliance best practices.
- Excellent analytical and problem-solving skills, with a focus on delivering scalable and efficient solutions.
- Strong communication skills, with the ability to effectively collaborate across teams and present technical concepts to non-technical stakeholders.
- Demonstrated ability to prioritize and manage multiple projects in a dynamic environment.
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