Senior Data Engineer

Job Responsibilities:

• Lead and drive the full data engineering lifecycle, including architectural design, conceptualization, data modeling, implementation, and operational management of complex data systems.
• Architect, build, and maintain robust, highly scalable data infrastructure, including real-time and batch ETL/ELT data pipelines, utilizing orchestration tools like Apache Airflow.
• Drive the development and continuous improvement of the organization's lakehouse and data warehouse by designing advanced data models and applying modern data transformation practices with tools like dbt.
• Design and automate scalable data processes by integrating diverse systems, APIs, and third-party services to ensure fault-tolerant data flow and synchronization.
• Define and enforce best practices for data governance, quality, and lineage, while establishing comprehensive monitoring and alerting frameworks to ensure system reliability at scale.
• Partner with cross-functional technical and business teams to translate complex business requirements into scalable technical specifications and robust production-ready solutions.
• Architect, deploy, and maintain internal automation solutions and data products. Integrating AI or machine learning components into these solutions to drive efficiency is a strong plus.
• Mentor junior data engineers, conduct code reviews, and provide technical leadership to elevate the team's data engineering practices.
• Any ad hoc duties as assigned.

Job Requirements:

• Bachelor's or Master's degree in Computer Science, Software Engineering, or related field is preferred.
• Proven track record and deep understanding of software engineering principles, scalable system architecture, and object-oriented design, with significant experience building enterprise-grade data applications will be a bonus.
• Expert-level proficiency in Python, including extensive experience with data manipulation libraries (e.g., Pandas, NumPy) and frameworks for web scraping (e.g., Selenium). Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch) is a plus.
• Advanced experience designing and deploying complex data pipelines using workflow orchestration tools like Apache Airflow.
• Deep expertise with data transformation tools like dbt and a mastery of modern ETL/ELT design patterns.
• Extensive experience with advanced data modeling techniques (conceptual, logical, physical) and data warehouse schemas (e.g., Star, Snowflake) for large-scale datasets.
• Significant hands-on experience and architectural knowledge of cloud platforms (GCP, AWS, or Azure).
• Highly proficient in SQL with extensive experience in designing, tuning, and optimizing relational databases and modern cloud data warehouses (e.g., BigQuery, Redshift, Snowflake).
• Strong practical experience with containerization (Docker, Kubernetes) and designing robust CI/CD pipelines.
• Experience in building AI agents, implementing GenAI solutions in production (AIOps), or developing solutions using Generative AI frameworks (e.g., ADK, LangChain, LlamaIndex) is a advantages.
• Excellent communication and leadership skills, with the ability to articulate complex technical concepts clearly to diverse stakeholders and executive teams.
• A highly proactive and analytical approach to solving complex problems, combined with meticulous attention to detail.
• Strong sense of ownership and autonomy, with a proven ability to independently lead major technical initiatives, manage complex projects, and deliver high-impact results.