Sr Data Engineer (Only W2)

The data engineering role will be responsible for implementing data solution for ad-hoc requests from end-to-end including but not limited to requirement gathering, gap analysis of the existing data platform and deploying the solution into the production environment. This role also includes coordinating with the Devops team and functional leads to ensure conformity to existing standards.
Lead the design, architecture, and implementation of key data initiatives and platform capabilities while ensuring alignment with business needs and scalability requirements.
Optimize existing data workflows and systems to improve performance, cost-efficiency, identifying and guiding teams to implement innovative solutions.
Lead and mentor a team of 2-5 data engineers, providing guidance on technical best practices, career development, and initiative execution.
Lead cross-functional projects, collaborating with product managers, analysts, solutions architects, and other stakeholders to define requirements, prioritize tasks, and deliver high-quality solutions.
Communicate effectively with non-technical stakeholders to understand business needs, translate them into technical specifications, and present technical concepts in a clear and concise manner.
Stay up-to-date with the latest data engineering technologies, trends, and best practices, evaluating and recommending new tools and approaches to immediate teams and the broader organization.
Contribute to the development of data engineering standards, processes, and documentation, promoting consistency and maintainability across teams while enabling business stakeholders.
Bachelor s degree or equivalent in Computer Science, Mathematics, Software Engineering, Management Information Systems, Computer Engineering/Electrical Engineering, or any Engineering field or quantitative discipline such as Physics or Statistics.
Minimum 6 years of relevant work experience in data engineering, with at least 2 years in a data modeling.
Strong technical foundation in Python, SQL, and experience with cloud platforms (for example, AWS, Azure,).
Deep understanding of data engineering fundamentals, including database architecture and design, Extract, transform and load (ETL) processes, data lakes, data warehousing, and both batch and streaming technologies.
Experience with data orchestration tools (e.g., Airflow), data processing frameworks (e.g., Spark, Databricks), and data visualization tools (e.g., Tableau, Power BI).
Proven ability to lead a team of engineers, fostering a collaborative and high-performing environment.
Good communication, interpersonal, and presentation skills, with the ability to effectively communicate with both technical and non-technical audiences.