Data Integration Architect

Are you an "emerging architect" who bridges the gap between high-level enterprise design and hands-on data engineering?

We are looking for a Data Integration Architect / Lead to take ownership of our end-to-end application-to-application data flows. In this role, you won't just be executing tasks, you will be designing the logic, rationale, and architecture for large-scale data movement across multiple ecosystems. If you are passionate about cloud modernization and building scalable data pipelines, this is the role for you!

What You Will Do

• Design & Own the Architecture: Architect data ingestion pipelines across multiple systems (e.g., SAP, legacy platforms) into modern data platforms (Snowflake, Databricks).
• Drive Platform Modernization: Lead the design and integration strategy for modernizing our data ecosystem from Azure Synapse to AWS Databricks, including hybrid/co-existence patterns.
• Define Integration Patterns: Apply batch, near-real-time, and event-driven integration patterns for optimal data movement (avoiding unnecessary heavy loads).
• Cross-System Enablement: Enable seamless integration between source systems, data platforms, and downstream consumption layers (e.g., Data Science platforms like SageMaker).
• Guide & Build: Act as the bridge between Enterprise Data Architects and Data Engineering teams. You will review designs, guide best practices, and assist in hands-on build/implementation and monitoring when necessary.

What You Bring

• Experience (10–15 years): Ideally, you have 10 to 15 years of total professional experience.
• Relevant Background: 8+ years in data integration/engineering, with at least 1.5 to 3 years specifically in a Data Architect or Solution Design role.
• The Right Mindset: You are not a pure task-executing Data Engineer, nor are you a purely theoretical Enterprise Architect. You possess an "architectural mindset" with the ability to clearly explain your design decisions while remaining hands-on.
• Enterprise Scale: Proven experience working in large-scale enterprise environments and designing end-to-end data flows.

Technical Skills Requirements

Must-Haves

• Cloud Platform: AWS is mandatory (Core services: storage, compute, networking).
• Data Architecture: Strong expertise in data integration architecture, ETL/ELT frameworks, and logical/physical data modeling.
• Processing: Solid hands-on experience with Spark / PySpark.
• Orchestration: Experience with tools like Airflow, ADF, or equivalent CI/CD pipelines for data.

Good-to-Haves

• Experience with cross-cloud setups (AWS ↔ Azure).
• Legacy knowledge of Azure Synapse Analytics.
• Familiarity with API integrations (REST/OpenAPI), messaging, and file-based integrations.
• Experience with SAP data integration.
• Exposure to Data Science platforms (SageMaker, DataRobot) and Data Governance/Lineage concepts.