Data Platform Architect

Data Architecture Design & Strategy :

○ Lead engagements with the internal stakeholders to understand their data needs and design solutions ensuring data quality and governance.

○ Design the right pipeline architecture to handle data and support various use cases, including analytical reporting and machine learning.

○ Design and evolve scalable and modular enterprise data architecture (including data lakes, data warehouses, real-time streaming, and operational data stores) to support current and future data needs.

○ Lead end-to-end architecture for data platforms including ingestion, storage, modeling, access, and integration.

○ Define and enforce data modeling standards and practices (e.G., star / snowflake schema, 3NF, Data Vault), ensuring consistency, maintainability, and performance.

○ Partner with cross-functional teams (Engineering, Analytics, Data science, Product, Sales, Marketing etc) to translate business requirements into reliable and robust data architectures.

○ Design architecture to support both batch and real-time processing needs using tools like Kafka, dbt, Airflow, etc

○ Evaluate and integrate emerging technologies, open-source tools, and vendor solutions to improve architectural robustness and delivery efficiency.

Minimum Qualifications :

We realize applying for jobs can feel daunting at times. Even if you don’t check all the boxes in the job description, we encourage you to apply anyway.

• 10+ years of experience designing and implementing enterprise-scale data solutions, including data warehouses, data lakes, and real-time systems.
• Hands-on of hands-on experience building and orchestrating ETL / ELT data pipelines using tools such as dbt, Apache Airflow, Glue, Airbyte, Matillion, or Stitch.
• 4+ years of experience in data modeling, schema design, performance optimization, and database architecture for both OLTP and OLAP systems.
• Strong proficiency in SQL, with demonstrated ability to write, debug, and optimize complex queries for performance and scalability.
• Solid programming skills in Python (preferred) or Java, with experience in building data transformation and integration logic.
• Deep understanding of AWS data services (such as S3, RDS, DynamoDB, Glue, Lambda, EMR, ECS) and cloud data warehouse platforms like Snowflake.
• Expertise in performance tuning, pipeline debugging, and optimizing data workflows in large-scale environments.
• Experience deploying and maintaining enterprise data platforms that support analytics, BI, and machine learning use cases at scale.
• Familiarity with distributed data processing frameworks such as Apache Spark, Hadoop, and Apache Kafka for real-time and big data processing.
• Strong foundation in software engineering best practices, including version control, CI / CD, modular design, and testing.
• Proven ability to lead and deliver complex data architecture projects in cloud-native environments, with excellent stakeholder and cross-functional collaboration skills.
• Strong analytical thinking, communication, and technical leadership abilities.
• Good understanding of software engineering principles and standards.