Job Description

We are embarking on a new strategic initiative for one of our US based clients, a leader in the auto insurance industry. We seek a Data Architect to play a key role in designing and delivering scalable, cloud-based data architectures. You will define and govern data models, oversee data pipelines and storage solutions, and ensure strong data governance, security, and compliance. You will work closely with clients and stakeholders, align data strategies with business needs, advise on Data & AI roadmaps, and contribute to innovation through best practices and team enablement.

You might be our missing piece if you have:

• 5+ years of experience in data architecture and modeling, ideally in large-scale, enterprise environments.

• Deep experience with cloud platforms (AWS, Azure) and modern data stack tools (e.g., DBT, Redshift, Snowflake, BigQuery, Airflow).

• Strong command of data modeling techniques (dimensional, normalized, hybrid), including experience with data warehouse/lakehouse environments.

• Solid understanding of ETL/ELT pipelines, version control, orchestration, and CI/CD in data workflows.

• Familiarity with data privacy and compliance standards, and experience implementing data governance frameworks.

• Excellent verbal and written communication skills; ability to convey complex concepts to technical and business audiences.

• Experience working in Agile development environments.

We would be thrilled if you have:

• Strong understanding of data lake and cloud-based data solutions.

• Familiarity with high-performance query optimization techniques.

• Exposure to AI/ML project workflows and their data preparation needs.

We will be working together on:

• Data Architecture & Strategy – Design and implement scalable, cloud-based data architectures. Oversee data pipelines, modeling, and storage using tools like DBT, Redshift, Snowflake, or BigQuery. Align solutions with business needs and build strong client relationships.

• Data Modeling – Develop and govern conceptual, logical, and physical data models following best practices (Kimball, Data Vault, 3NF). Ensure models are scalable, maintainable, and well-documented.

• Governance, Security & Compliance – Establish data governance strategies for quality, security, privacy, and regulatory compliance (e.g., GDPR). Implement metadata management and lineage tracking.

• Client & Stakeholder Engagement – Act as a strategic partner, advising clients on Data & AI roadmaps, emerging technologies, and best practices. Facilitate architecture reviews and cross-functional collaboration.

• Innovation & Enablement – Stay ahead of industry trends, mentor teams, and contribute to reusable frameworks, documentation, and best practices.