Chief Data Architect

2 days ago

Quezon City, National Capital Region, Philippines beBeeDataEngineer Full time ₱800,000 - ₱1,200,000
Job Description:

As a Data Engineer, you will design, build, and maintain scalable data pipelines to extract, transform, and load (ETL) data from various sources into data warehouses or data lakes.

You will manage and optimize data infrastructure across hybrid cloud environments, leveraging cloud-native services and on-premises resources.

Ensuring data quality through implementation of data validation, cleansing, and standardization processes is crucial.

Developing interactive reports and dashboards using tools like Power BI, Tableau, or Looker to provide actionable insights to stakeholders is also an essential part of this role.

Adhering to data governance policies and procedures, including data security, privacy, and compliance regulations, is necessary for success in this position.

Required Skills and Qualifications:
  1. Experience as a Data Engineer or similar role with a focus on data pipelines, cloud infrastructure, and reporting.
  2. Strong understanding of data engineering concepts, tools, and technologies (e.g., SQL, Python, ETL tools, cloud platforms).
  3. Experience with major cloud platforms (e.g., AWS, Azure, GCP) and their data-related services (e.g., data warehouses such as BigQuery/Redshift, data lakes, data pipelines).
  4. Proficiency in data modeling techniques (e.g., dimensional, normalized) and data warehouse design.
  5. Expertise in using reporting and visualization tools (e.g., Looker, Power BI, Tableau) to create interactive dashboards.
  6. Ability to troubleshoot complex data-related issues and find innovative solutions.
  7. Excellent communication skills to collaborate effectively with cross-functional teams.
  8. Preferred certifications (e.g., AWS Certified Data Engineer, Azure Certified Data Engineer, GCP Certified Professional Data Engineer).

Additional Skills (Preferred):

  1. Experience with data warehousing and data lake technologies (e.g., Google BigQuery, Amazon Redshift, Snowflake, Databricks)
  2. Knowledge of data analytics and machine learning concepts
  3. Familiarity with data governance and compliance frameworks (e.g., HIPAA, GDPR, CCPA)

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