Data Architect

Datacrew; Sun City (AZ); On-site; USD 140,000 - 190,000; Full time

Job summary

Datacrew is seeking an experienced Data Architect to design scalable, secure data platforms across cloud environments and drive governance, security, and interoperability. You will lead data modeling, integration pipelines, and modernization from on-prem to cloud, collaborating with engineers, data scientists, and analysts to ensure architectural alignment with business goals.

The role emphasizes leadership in pre-sales, architecture discussions, and ongoing optimization for performance and cost

Qualifications

• Experience designing scalable, high-performance data platforms.
• Strong data modeling (conceptual, logical, physical) capabilities.
• Expertise in data governance, security, and compliance.

Responsibilities

• Design and architect scalable, secure data platforms (Data Warehouses, Data Lakes, Lakehouse).
• Leverage PySpark, Hadoop, Kafka, and Databricks for large-scale processing.
• Develop and maintain data models aligned with governance standards.
• Architect data integration pipelines across legacy and cloud platforms (Databricks, Snowflake, AWS, Azure).
• Lead modernization from on-premises databases to cloud-native platforms (AWS, Azure, Google Cloud).
• Optimize data pipelines for performance, scalability, cost, and SLA adherence.
• Collaborate with business teams to provide technical leadership in pre-sales engagements.
• Partner with data engineers, data scientists, and IT to align architecture with goals.
• Define and enforce governance frameworks, quality standards, and security policies.

Skills

Data modeling

Big Data architecture

Distributed processing

SQL

NoSQL

ETL/ELT

Cloud migration

Tools

PySpark

Hadoop

Kafka

Databricks

Snowflake

AWS

Azure

Google Cloud

Job description

We are looking for an experiencedData Architectto design and implement scalable, high-performance data platforms that align with our organization’s evolving business needs. The role involves leading data architecture design, integration, and optimization across cloud environments, while ensuring robust data governance, security, and scalability.

Responsibilities:

• Design and architect scalable, high-performance, and secure data platforms (Data Warehouses, Data Lakes, Lakehouse) to support analytics, AI/ML, and reporting needs.
• Leverage Big Data technologies such as PySpark, Hadoop, Kafka, and Databricks for large-scale data processing and transformation.
• Develop and maintain conceptual, logical, and physical data models aligned with business requirements and governance standards.
• Architect data integration pipelines to enable seamless data flow across diverse systems, including legacy and modern cloud platforms (Databricks, Snowflake, AWS, Azure).
• Lead modernization and migration initiatives from on-premises databases (e.g., Informix, Oracle) to cloud-native platforms (AWS, Azure, Google Cloud).
• Continuously optimize data pipelines and storage for performance, scalability, cost efficiency, and SLA adherence.
• Partner with business development teams to provide technical leadership during pre-sales engagements, including client workshops and architecture discussions.
• Collaborate with data engineers, data scientists, business analysts, and IT teams to ensure architecture alignment with business goals.
• Define and enforce data governance frameworks, quality standards, and security policies to ensure compliance with organizational and regulatory requirements.
• Evaluate emerging technologies and tools to define long-term data strategy and ensure future scalability.

Key Skills:

• Data modeling (conceptual, logical, physical) for high-performance platforms
• Big Data architecture and distributed processing (PySpark, Hadoop, Kafka)
• Relational (SQL Server, Oracle, MySQL) and NoSQL (DynamoDB, CosmosDB)
• ETL/ELT pipeline design, automation, and system integration
• Cloud migration: legacy to cloud transitions

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