Data Architect (Data Platform)

Data Modeling & Design

You need to be fluent in conceptual, logical, and physical data modeling. That includes understanding normalization vs. denormalization, dimensional modeling (star/snowflake schemas), and designing for scalability and performance.

Database & Storage Expertise

• Deep knowledge of both relational and non-relational systems is critical.
• Familiarity with data lakes, lakehouses, and distributed storage systems/warehouses (e.g., S3, Delta Lake, BigQuery).

Data Integration

Designing pipelines that move and transform data reliably. This includes experience with ETL/ELT tools (DBT), streaming systems (Kafka, Kinesis), and orchestration frameworks (Airflow, etc.). With the ability to understand batch vs. real-time tradeoffs.

Performance Optimization

• Indexing strategies, partitioning, query tuning, and workload.
• The ability to architect for scale, resiliency and business continuity.

Strategic Thinking

• Define what the future data architecture should look like
• Determine how and where to reduce technical debt
• Identify how to enable analytics insights, incorporate AI, and drive self-service?
• Define and evolve data architectures that support AI/ML workloads
• Define and evolve data architectures that leverage AI to drive greater operational efficiency
• Design scalable pipelines and platforms (e.g., lakehouse, streaming, feature stores) that enable faster data availability for AI-driven insights and real-time decision support

Minimum Requirements

• 12+ years of software engineering experience, including hands-on technical experience building, maintaining and scaling data systems.
• 5+ years of experience as a tech lead who successfully converts business / product requirements into well architecture designs.
• Extensive experience in building and scaling large data pipelines including real time processing and / or 100+ GB transformation in Java, Python, DBT, and SQL.
• Extensive experience in building and driving large business outcomes by leveraging a combination of existing and new technologies.
• A deep knowledge of common data technology stacks such as GCP BigQuery, Snowflake, Databricks, DBT, Datalake architecture on AWS S3 or GCP Cloud storage.
• A deep knowledge in cloud platforms such as AWS, GCP, or Azure, and cloud-native API solutions.
• A deep knowledge of data modeling and data governance control.
• Strong RESTful API design principle, microservices architecture, distributed asynchronous system and good design patterns.
• Strong knowledge with CI/CD pipelines (CircleCI, Github Action), containerization (Docker, Kubernetes), and version control (Git), infrastructure as code (Pulumi, Terraform), Relational and NoSQL databases, caching mechanisms (Redis, Memcached), and performance optimization techniques.
• Strong leadership and communication skills, with the ability to influence cross-functional teams and communicate complex technical details to upper management and non-technical stakeholders.
• Prominent problem-solving ability with a focus on delivering solutions.
• Positive attitude: Maintaining a constructive approach to work challenges.