Description

Define and lead end-to-end data solution architecture, including system design, data flows, and platform patterns.

This role is hybrid.

Responsibilities

• Architect scalable data platforms, defining batch vs. streaming ingestion, storage, and processing strategies.
• Build prototypes and guide engineering teams in developing robust data pipelines and models.
• Optimize data performance through partitioning strategies and query efficiency improvements.
• Establish CI/CD practices, reusable frameworks, and standards for data platform development.
• Implement security, governance, and compliance measures, including IAM, encryption, and data quality controls.

Required Skills

• 6+ years in Data Engineering or Data Architecture.
• Strong expertise in data modeling (Dimensional, Data Vault, schema design).
• Hands-on experience with cloud data warehouses: Snowflake, Redshift, or BigQuery.
• Proficiency in processing frameworks: Spark, Databricks, or PySpark.
• Experience with streaming technologies: Kafka or Kinesis.
• Advanced programming in Python or Scala, plus advanced SQL.
• Experience building enterprise-scale data pipelines using ETL/ELT and orchestration tools like Airflow or dbt.
• Strong knowledge of cloud platforms, with AWS preferred.
• Understanding of data security best practices, including IAM and encryption.

Preferred Skills

• Experience with infrastructure-as-code and containerization: Terraform, Kubernetes, Docker.
• Familiarity with data governance tools for lineage, cataloging, and data quality.
• Certifications in AWS Architect, Databricks, Snowflake, or CDMP.

Key Skills

Education

Bachelor's degree