Data Engineering Architect
J
Juniper Square FinTech
Americas (USA or Canada)Full-TimeLead
Salary210000 - 270000 USD per year
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSPythonSQLGCPJavaMicrosoft Power BITableauAzureSparkScaladbtLooker
Requirements
- Advanced degree in Computer Science, Engineering, or related field
- 10+ years in data engineering, analytics engineering, or data platform roles
- Proven experience architecting large-scale data and analytics systems
- Strong hands-on experience with modern data stacks in cloud environments
- Deep expertise in data modeling for analytics (dimensional, star/snowflake, Data Vault)
- Advanced SQL skills
- Proficiency in Python, Scala, or Java
- Advanced expertise in dimensional data modeling and semantic layers (dbt, Cube)
- Experience with distributed processing frameworks (Spark, Flink)
- Experience building reporting and BI solutions at scale
- Strong understanding of both batch and real-time architectures
- Hands-on experience with AWS, Azure, or GCP data services
- Experience with BI tools (Looker, Tableau, Power BI)
- Strong understanding of data governance and security best practices
- Ability to operate at both executive and deeply technical levels
Responsibilities
- Define and own the end-to-end data and analytics architecture strategy
- Design scalable batch, streaming, and real-time data systems
- Establish standards for data modeling, semantic layers, and reporting
- Lead architecture reviews and technical decision-making
- Drive adoption of modern architectures (lakehouse, data mesh, real-time analytics)
- Design and prototype critical data platform components
- Write production-quality code for complex or high-impact areas
- Review schemas, transformations, dashboards, and analytics models
- Troubleshoot performance and reliability issues across pipelines and queries
- Optimize workloads for latency, concurrency, and cost
- Design and architect a scalable data platform supporting ingestion, transformation, and delivery of both structured and unstructured data across batch and real-time pipelines
- Develop a RAG-ready data architecture that enables trusted enterprise data retrieval with strong lineage, governance, security, and observability
- Create curated data products and reusable APIs that make high-quality datasets easily consumable by applications, analytics platforms, and AI agents
- Ensure reliability, scalability, and resilience of the platform, including high availability, monitoring, and disaster recovery readiness
- Mentor senior engineers, analytics engineers, and data scientists
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