Function

Join our Data & Analytics organization, where we design and modernize enterprise-scale data platforms that power business intelligence, advanced analytics, AI/ML, and enterprise reporting.

You will collaborate with business stakeholders, product managers, solution architects, data engineers, and AI/ML teams to establish scalable data architectures and modern cloud-based data ecosystems. The team focuses on building secure, reliable, high-performance data platforms using modern cloud, Lakehouse, streaming, and data engineering technologies.

• Define and maintain enterprise data architecture, including conceptual, logical, and physical data models.
• Architect scalable cloud data platforms, data warehouses, data lakes, and lakehouse solutions.
• Lead architecture and implementation of ETL/ELT, batch processing, streaming, and real-time data integration solutions.
• Design enterprise data solutions supporting BI, analytics, AI/ML, and reporting initiatives.
• Partner with business stakeholders, product managers, architects, and engineering teams to translate business requirements into scalable technical architectures.
• Establish architecture patterns and standards for data ingestion, transformation, storage, processing, integration, and consumption.
• Design distributed, scalable, highly available, and high-performance data architectures.
• Ensure data platforms meet requirements for performance, reliability, security, monitoring, automation, and availability.
• Provide technical leadership to data engineering teams and guide implementation of enterprise data solutions.
• Support event-driven architectures, APIs, microservices, and modern data integration patterns.
• Drive engineering best practices across Agile, CI/CD, DevOps, containerization, and Infrastructure as Code.
• Evaluate emerging technologies and recommend platforms, tools, and architecture improvements to modernize the enterprise data ecosystem.
• Collaborate with AI/ML and analytics teams to enable data consumption for advanced analytics and AI use cases.

• 10+ years of experience across data architecture, data engineering, or enterprise data platform development.
• 5+ years leading data engineering initiatives and delivering enterprise-scale data solutions.
• Proven experience designing and owning enterprise data architecture, not just developing individual pipelines.
• Bachelor's degree in Computer Science, Information Systems, Engineering, or related discipline.

• Strong expertise in conceptual, logical, and physical data modeling.
• Deep knowledge of data warehousing, ETL/ELT, Data Lakes, and Lakehouse architectures.
• Experience designing distributed, scalable, high-performance data platforms.
• Strong understanding of batch, streaming, and event-driven data processing.

• Strong experience with AWS.
• Experience with AWS data services including S3, EMR, Glue, and Redshift.
• Strong experience with Snowflake or comparable modern cloud data platforms.

Hands-on/architecture experience with:

Spark | Kafka | Airflow | Talend | dbt | Python | SQL

Experience across relational and NoSQL platforms such as:

SQL Server | PostgreSQL | Amazon Aurora | DynamoDB

Strong understanding of:

Data Lake/Lakehouse | Event-Driven Architecture | Microservices | REST APIs | Data Modeling

Experience with:

Kubernetes | Docker | Git | CI/CD | Infrastructure as Code

Strong understanding of Agile delivery, DevOps, automation, monitoring, and operational best practices is expected.

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