AWS Data Lake Architect

AWS Data Lake Architect

AWS Data Lake Architect

Key Responsibilities

• Platform Takeover and Optimization

  : Assume responsibility for our existing AWS-based data lake, which has been in production for three years. Conduct a comprehensive review of the current architecture, identify areas for improvement in performance, cost, and security, and lead the technical roadmap for optimization.

• In-House Team Building

  : Recruit, hire, and mentor a new team of data engineers and developers. Establish best practices for data engineering, CI/CD, and operations to ensure the team can effectively manage the data platform.

• Architectural Evolution

  : Evolve the existing data lake architecture to meet the growing needs of the business. This includes designing and implementing new data pipelines, integrating new data sources, and upgrading existing services to leverage the latest AWS features.

• Hands-on Development

  : Act as a lead engineer, contributing to key development tasks, building core data pipelines, and creating infrastructure-as-code (IaC) templates. You will set the technical standard for the team.

• Collaboration and Strategy

  :  Work closely with cross-functional teams, including product managers, data scientists, and business stakeholders, to understand requirements and deliver data solutions that drive business value.

• Governance and Security

  : Enhance and enforce data governance, quality, and security frameworks to ensure the integrity and compliance of our data assets.

• Mentorship & Best Practices

  : Act as a subject matter expert, staying up-to-date with the latest AWS services and industry trends, particularly in the AI space. Champion best practices for data modeling, ETL/ELT, and data pipelines across the organization.

Qualifications

• Experience

  : 5-8 years of progressive experience in data architecture, with at least 2-3 years in a leadership or senior architect role.

• Platform Ownership

  : Proven experience in taking over, managing, and optimizing a complex data platform from a third-party provider or external team.

• AWS Expertise

  : Deep, hands-on expertise with AWS data services, including:

• Data Ingestion & Storage

  : S3, Glue, Kinesis, DMS, Lambda.

• Data Processing & Warehousing

  : EMR, Athena, Redshift, Spark.

• Orchestration & Automation

  : Step Functions, CloudFormation, Terraform.

• Security & Governance

  : IAM, KMS, Lake Formation.

• Technical Skills

  : Strong proficiency in Python and SQL. Experience with big data technologies and distributed computing frameworks.

• Leadership

  : Proven experience in leading and mentoring a technical team, with excellent communication and interpersonal skills.

• Education

  : Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field. AWS professional-level certifications (e.g., AWS Certified Data Analytics - Specialty, AWS Certified Solutions Architect - Professional) are highly preferred.