Data Architect

Data Architect We're looking for a Data Architect who can design modern cloud data platforms while staying hands-on with the engineering work — someone comfortable moving between architectural decisions and writing the pipelines that implement them. What we're looking for You've worked as a hands-on data engineer building and maintaining production-grade data solutions, and you've grown into architecture — assessing existing client environments, designing scalable platforms, and guiding technical decisions from design through implementation. You're not looking for a purely conceptual or oversight role. You're comfortable in client-facing settings, translating business requirements and technical constraints into practical solutions, and you can hold your own in discovery conversations, solution design, and where applicable, pre-sales or proposal support. You have depth in at least one major cloud provider (AWS, Azure, or GCP), plus familiarity with data warehousing and distributed processing. You're also flexible about how engagements start — often in an engineering capacity, taking on deeper architectural ownership as the solution and team mature. Skills we're looking for (most, not all) Cloud platforms & warehouses — Redshift, Snowflake, BigQuery, Azure Synapse, EMR Big data & streaming — Spark, Presto, Databricks, Kinesis, Kafka Workflow & orchestration — Airflow, dbt, Dagster, Azure Data Factory Databases, containers & ML — DynamoDB, Cosmos DB, MongoDB, Kubernetes, ECS, SageMaker, Azure ML Studio Languages — Python, Java Nice to have Experience with AI-accelerated data engineering — using generative or AI-enabled tools to improve the speed and quality of architecture and engineering work Experience mentoring and developing other data architects and engineers What you'd be doing Designing and evolving data architectures for client environments, contributing directly to building pipelines and platforms alongside the team, and guiding technical direction without stepping away from the day-to-day work. The focus is data engineering and architecture outcomes — minimal BI or visualization work.