Responsibilities

• Architect and evolve our data platform using Snowflake as the central data warehouse and dbt for transformation logic.
• Design and implement batch and streaming pipelines, ensuring scalability and cost-efficiency in AWS.
• Establish best practices for data modeling, warehouse schema design, and semantic layer standardization using dbt.
• Define and enforce data governance, security, and privacy policies in alignment with compliance frameworks.
• Lead data migration from legacy sources to AWS; work closely with DevOps to automate and secure deployment processes.
• Collaborate with engineering, product, and business stakeholders to understand data needs and align solutions with business goals.
• Mentor and guide data engineers; support code reviews, architectural reviews, and team upskilling.

Qualifications:

• 8+ years in data architecture, with deep experience owning a modern data platform end to end: pipelines, storage, modelling, governance, monitoring.
• Expertise in data modeling (3NF, star/snowflake schemas), ELT/ETL design, and performance tuning.
• Solid understanding of AWS services (S3, Glue, Lambda, Redshift, Kinesis, etc.).
• Strong programming in SQL and Python; experience with CI/CD and data testing in dbt.
• Knowledge of data governance, lineage, metadata management, and data quality frameworks.
• Experience working with streaming technologies (Kafka, Kinesis) and large-scale datasets.
• Has modernised platforms and run migrations while keeping live systems running.
• Brings documentation, data quality, observability and testing as a default while balancing scalability, performance, maintainability and cost.
• Designs architecture and makes the technology calls, not just implements them. Sets common patterns and standards across application teams.
• Works comfortably across both engineering and business.

Preferred Skills:

• Experience in platform modernization and cloud migration projects.
• Familiarity with tools like Airflow, Terraform, Great Expectations, or Monte Carlo.