Principal Data Architect

isolved•,

1d•Remote

About The Position

The Principal Data Architect is isolved's senior authority on how data is structured, classified, secured, and moved across our systems, from ingestion through downstream consumption. This is a companion role to the Principal Platform Architect, focused not on the platforms themselves but on the data that flows through them: its structure, flow, lineage, integrity, and cleanliness. The successful candidate brings a rare combination of deep big data, data warehouse, data science, and data analytics experience, with fluency in modern data architecture patterns such as medallion architecture, data mesh, data cubes, and materialized views. This person can trace a piece of data end to end, from source ingestion through transformation, storage, and downstream consumption, and design the architecture that keeps that journey scalable, trustworthy, and well-governed. Excellent soft skills and customer-facing experience are important for this role, as is comfort operating as an expert coder and AI user across isolved's data tooling ecosystem.

Requirements

Bachelor's degree in computer science, data engineering, information systems, or related field, or equivalent practical experience.
Solid experience with Databricks (Azure preferred) with granular product and platform knowledge.
Deep experience across big data, data warehousing, data science, and data analytics. This is a rare and highly valued combination for this role.
Expert-level SQL, with strong hands-on experience in PySpark and Python.
Strong understanding of medallion architecture, data mesh, data cubes, and materialized views.
Demonstrated experience across the full data pipeline lifecycle, including ingestion, transformation, storage, and downstream consumption.
Experience with Git and GitHub Actions for version control and CI/CD.
Working familiarity with Terraform and Azure infrastructure, including subscriptions, resource groups, compute, and identity and access management. Deep infrastructure expertise is not required, but hands-on familiarity is a plus.
Excellent soft skills, with customer-facing experience strongly preferred.
Expert use of AI tools in day-to-day development and data work.
Experience with data governance and cataloging tools, such as Microsoft Purview, Collibra, or Alation.
Familiarity with data quality and testing frameworks, such as Great Expectations or dbt tests.
Solid grasp of dimensional data modeling techniques, including star and snowflake schemas.
Understanding of data privacy and compliance considerations relevant to HR/payroll data, including PII handling, SOC 2, GDPR, and CCPA.
Familiarity with streaming/real-time data technologies (e.g., Kafka, Event Hubs, or similar) as a complement to batch pipeline experience.
Understanding of data lifecycle management, retention/archival policies, and storage/compute cost optimization.
Experience with Snowflake as a cloud data warehouse platform, including data modeling and performance optimization within it.
Experience with Airflow or equivalent workflow orchestration tools for sequencing and monitoring data pipelines.
Experience with data sharing technologies and patterns, such as Snowflake Secure Data Sharing or Databricks Delta Sharing, including the governance and access considerations they require.
Strong presentation and stakeholder communication skills, comfortable engaging both technical and executive audiences.

Nice To Haves

Tenured experience in dbt, Fiveran and HighTouch desired.
Experience with BI and visualization tools such as Power BI or Tableau is a plus.
Preferred: hands-on experience with big data ecosystem services such as Hadoop, Hive, HBase, YARN, and Spark, reflecting deep exposure to large-scale distributed data platforms.
Experience with the following a plus (not required): Airflow or equivalent orchestration tools, Parabola.io or Workato, Big data ecosystem services such as HDFS, Hive, HBase, YARN, and Spark

Responsibilities

Architect and govern isolved's data structures, classifications, and flows across the full data lifecycle, from ingestion through downstream consumption.
Design and evolve data architecture patterns, including medallion architecture, data mesh, data cubes, and materialized views, appropriate to isolved's scale and use cases.
Own data lineage, integrity, and cleanliness standards, and partner with engineering teams to implement and enforce them.
Architect and optimize data pipelines built on isolved's core data tooling: Databricks, Snowflake, dbt, Fivetran, and Hightouch.
Design pipeline orchestration patterns, using Airflow or equivalent tooling, to sequence and monitor data workflows across ingestion, transformation, and delivery.
Define governance, access controls, and lineage tracking for data shared externally or across teams, using technologies such as Snowflake Secure Data Sharing or Databricks Delta Sharing.
Partner with the integrations team on the data architecture underpinning Parabola.io and Workato-based integration workflows.
Translate business and analytical needs into well-structured data models, warehouses, and pipelines, applying dimensional modeling techniques such as star and snowflake schemas where appropriate.
Write production-quality code, including PySpark, Python, and SQL, to build, optimize, and maintain data pipelines and architecture.
Use Git and GitHub Actions to manage version control and CI/CD for data pipeline code and infrastructure.
Apply working knowledge of Terraform and Azure infrastructure (subscriptions, resource groups, compute, and IAM) to understand and inform the infrastructure underpinning isolved's data systems.
Establish and maintain data governance practices, including data cataloging, metadata management, and master data management standards.
Partner with security and compliance stakeholders to ensure data handling practices meet privacy and regulatory requirements relevant to HCM and payroll data, such as PII protection, SOC 2, and GDPR/CCPA considerations.
Design performant data models and semantic layers that support downstream BI and reporting tools.
Extend data architecture patterns to real-time and streaming use cases where appropriate, alongside isolved's core batch pipelines.
Define data lifecycle, retention, and archival practices, and factor storage and compute cost optimization into data architecture decisions.
Serve as a customer-facing and cross-functional voice on data architecture, translating complex technical concepts for both technical and non-technical stakeholders.
Evaluate and apply AI development practices to data architecture, tooling, and automation.
Document data architecture standards, lineage, and governance practices, and mentor engineers on data best practices.