Data Engineering

Responsibilities:
1) Design, develop, and maintain robust ELT/ETL pipelines to ensure seamless data flow.
2) Optimize and manage data storage solutions, including data warehouses and data lakes, to enhance performance and scalability.
3) Construct and refine data models that align with engineering and business requirements.
3) Implement and monitor data quality measures, ensuring accuracy and consistency across the data ecosystem.
4) Continuously enhance data architecture to support the organization's evolving data needs.
5) Collaborate with cross-functional teams to drive data-driven decision-making and strategic initiatives.
6) Develop REST API services for data access when required.
7) Provide expert guidance to clients on data engineering best practices and cloud platform utilization.
8) Pursue and maintain relevant certifications to stay current with industry standards.
9) Stay informed about the latest advancements in data engineering and cloud-based data platforms.
Preferred Skills:
1) Orchestration Frameworks: Experience with tools like Apache Airflow, Apache Beam, or similar.
2) Cloud Platforms: Familiarity with Google BigQuery, DataProc, or other cloud-based data platforms.
3) Data Governance: Understanding of data governance and data quality practices.
4) Software Development: Knowledge of SDLC, CI/CD, DevOps practices, Git, and containerization technologies.
5) Platform Tooling: Experience with Kubernetes, Terraform, or similar tools.
6) Visualization Tools: Proficiency with visualization tools such as Tableau or Looker is a plus.
7) Customer Data Platforms: Experience working with Customer Data Platforms is advantageous.