Senior/Lead Cloud and Data Architect

Senior/Lead Cloud and Data Architect

Key Responsibilities

Leadership and Collaboration:

data engineers

Act as the central technical point of contact for enterprise-wide data and analytics initiatives, ensuring alignment with business objectives.

Collaborate with senior leadership and business stakeholders to define and deliver on data strategy, ensuring scalability and alignment with enterprise goals.

Mentor and upskill team members, providing guidance on best practices, emerging technologies, and professional development.

Enterprise Architecture:

Develop and maintain enterprise-level architectural blueprints for cloud and data platforms, ensuring interoperability and scalability.

data platforms

Provide strategic input on technology roadmaps, ensuring alignment with organizational vision and future-proofing investments.

Evaluate and recommend emerging technologies and frameworks to enhance enterprise data capabilities.

Cloud and Data Platform Engineering:

AWS

Databricks

Lead the development of end-to-end data pipelines, ensuring robust integration between data sources, platforms, and analytics tools.

Data and ML Integration:

Collaborate with ML teams to deploy machine learning pipelines and operationalize AI models within enterprise systems.

Provide architectural support for ML initiatives, including data preparation, feature engineering, and model lifecycle management.

Enable seamless integration of ML and analytics into business workflows, enhancing decision-making and operational efficiency.

Data Warehousing and Modeling:

Architect enterprise data warehouses with robust data models, ensuring high performance and reliability for analytics workloads.

Lead the development of advanced data models that cater to both analytical and operational requirements, emphasizing scalability and data quality.

DevOps and DataOps Practices:

DevOps

Drive initiatives for continuous improvement in data delivery, ensuring high availability, data quality, and scalability.

Governance, Security, and Compliance:

Define and enforce data governance policies, including data security, lineage, and compliance with industry regulations.

Implement robust security measures to protect sensitive data across platforms, adhering to privacy standards such as GDPR and CCPA.

Qualifications:

Education and Experience:

Education

Experience

o10+ years in data architecture, cloud platforms, and advanced analytics.

AWS

Databricks

data warehousing

ML pipelines

oProven track record in enterprise architecture, aligning technology solutions with business goals.

Technical Skills:

Python

Familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-learn is a plus.

Deep understanding of big data frameworks, distributed systems, and real-time processing.

Terraform

Leadership Skills:

Strong strategic thinking with the ability to define and execute large-scale data and cloud strategies.

Exceptional communication skills to engage with both technical and non-technical stakeholders.

Proven ability to lead diverse teams across geographies and time zones, ensuring high performance and cohesion.

Capable of making data-driven decisions while managing competing priorities and deadlines.

Preferred Qualifications:

Certifications in AWS, Azure, GCP, Databricks, or Snowflake.

Experience working in enterprise-scale environments with complex data landscapes.

Exposure to industry-specific data challenges in domains like finance, insurance, or healthcare.