Azure Data Architect

Overview

When you think of InComm Payments, think of Innovative Payments Technology. We were founded over 30 years ago and continue to be a pioneer in the payment (Fin Tech) industry. Since our inception, we have grown to be a team of over 3,000 employees in 35 countries around the world. We own over 400 global technical patents and a network that includes over 525,000 points of retail distribution that points to our industry expertise.

InComm Payments works with the most recognized and valued brands in the world, and we are partnered with most of the world’s leading merchants. InComm Payments is highly focused on our people and their growth, and we work hard to make a career at InComm Payments meaningful and rewarding. We value innovation, quality, passion, integrity, and responsibility in all that we do, and we are looking for great people to join our team as we move forward towards a very bright future.

You can learn more about InComm Payments by visiting our Website or connecting with us on Linked In, You Tube, Twitter, Facebook, or Instagram.

We are seeking a technically strong Azure Data Architect with deep hands-on expertise in architecting and building enterprise‑grade Azure data platforms. This role focuses on designing and optimizing scalable data solutions—particularly in multi‑tenant Azure environments—while closely partnering with business teams to translate requirements into modern, efficient, and maintainable data products.

The ideal candidate brings strong knowledge in Azure Synapse (Dedicated & Serverless SQL pools), ADLS Gen2, Spark/PySpark Notebooks, Azure Data Factory, Logic Apps, Automation Accounts, and related Azure‑native data services. They will be responsible for engineering robust ingestion, transformation, and orchestration pipelines; implementing sound data governance practices; and ensuring data environments are secure, performant, and scalable.

This role blends deep technical acumen across modern data architectures with the ability to collaborate cross‑functionally, ensuring the Azure platform delivers measurable business value through trusted, high‑quality data pipelines and analytics capabilities.

Based on the location of our team members, Eastern timezone work hours are expected.

• Design, architect, and implement a scalable, multi‑tenant Azure Data Lake and analytics ecosystem using ADLS Gen2, Synapse Analytics, SQL Dedicated Pool, Serverless SQL, and Delta Lake patterns.
• Build and optimize end‑to‑end data pipelines using Azure Data Factory, Synapse Pipelines, Logic Apps, Azure Functions, and Automation Accounts for orchestration and operational tasks.
• Develop, operationalize, and optimize PySpark / Spark Notebooks running in Synapse or Azure Databricks for large‑scale batch and near‑real‑time data transformations.
• Architect and implement modern ingestion frameworks using Event Hub, Kafka‑on‑Azure, and ADF Mapping Data Flows to support streaming and batch workloads.
• Design secure, governed multi‑tenant environments including tenant‑boundaries, RBAC, managed identities, data access policies, and Purview‑based metadata governance.
• Develop well‑structured analytical models (Star, Snowflake, Data Vault) using Synapse, SQL Database, and Serverless SQL endpoints to support BI, reporting, and ad‑hoc analytics.
• Implement best practices for data quality, cataloging, lineage, security, CI/CD, Infrastructure‑as‑Code, and Azure Dev Ops‑based deployment workflows.
• Optimize cloud spend through storage tiering, workload separation, SQL Pool optimization, and serverless query tuning.
• Administer and tune Azure SQL resources including SQL Dedicated Pools, SQL Database, and Serverless SQL—including T‑SQL development, partitioning, indexing, and performance tuning.
• Collaborate with cross‑functional teams to translate business needs into scalable Azure data platform solutions.

• Strong experience designing and building Azure‑based data platforms
  , including ADLS Gen2, Synapse, ADF, Event Hub, Azure SQL, Databricks, or Fabric.
• Extensive hands‑on experience building enterprise‑scale Data Lakes, ingestion frameworks,…