At Ideas2IT, Data Architects don't inherit broken pipelines and patch them in the dark. You'll design data platforms from the ground up – the kind of work we've done for companies like Meta, Bloomberg, and Medtronic, and for our own AI-native products that have spun off as VC-funded startups. If you've been craving work where your architecture decisions actually matter, where the data you move powers everything from enterprise AI platforms to personalized cancer treatment, this is that role.

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‍What you'll actually do as a Data Architect with us:

You'll own the full arc of data platform engineering, from whiteboard to production, across cloud environments at scale.

• Design and build cloud-native data platforms on AWS, Azure, or GCP (and increasingly, across all three clouds)
• Design and implement lakehouse architectures (bronze/silver/gold) that unify data lakes and warehouses for scalable analytics
• Model data that balances clean abstraction with real-world query performance
• Build and tune data warehouses: Redshift, Snowflake, Postgres, for analytics and reporting at scale
• Design and build event-driven data systems and real-time pipelines
• Define and enforce data governance practices including quality, lineage, cataloging, and access control to ensure trusted and secure data 
• Orchestrate workflows with Airflow or Dagster, because pipelines without orchestration are just scripts waiting to fail
• Write production-grade Python for data processing, automation, and system integration 
• Translate stakeholder needs into technical specs and communicate them back without the jargon wall
• Bring ML and data science awareness to your designs: you don't need to build the models, but you need to understand what they need
• Optimize data platforms for cost and performance across cloud environments
• Provide technical leadership and mentorship to data engineering teams, setting best practices and architectural standards

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Must-haves:

• Experience designing end-to-end data architectures   and shipping data platforms in cloud environments
• Solid SQL, data modeling, and data warehousing fundamentals — these are non-negotiable
• Deep expertise in AWS Data Engineering or Azure Data Engineering (ideally both, GCP familiarity is a bonus)
• Strong Spark experience — you've processed data at scale and know where the bodies are buried
• Experience with at least one major ingestion tool: Kafka, NiFi, Glue, or Data Factory
• Strong understanding of data governance, security, and compliance principles
• Orchestration experience with Airflow or Dagster
• Experience optimizing cost and performance in cloud data platforms‍
• Python proficiency; Pandas is a plus

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Nice-to-haves:

• Experience with lakehouse architectures (Delta Lake / Iceberg)
• Experience implementing CI/CD for data pipelines
• Hands-on experience with Tableau or Power BI
• DevOps mindset — you think about how your data systems get deployed, monitored, and maintained
• Understanding of ML workflows and what good data infrastructure looks like for AI applications

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Why consider Ideas2IT – and why now?

We sit at the intersection of a platform-led product engineering firm and a product studio. That means you get variety; real variety, across industries, tech stacks, and problem types. No two years here ever look the same. We've also launched six of our own products and platforms through IdeaLabs, several of which have become independently funded startups: Element5 (a $50M health-tech platform), LegacyLeap, IdeaRx, PipeCandy, and more. Data architecture sits at the heart of all of it. AI-native development is reshaping what great engineering looks like. The engineers who thrive in the next phase won't just write code, but will orchestrate AI tools and agents to handle routine work, freeing themselves for system design, product direction, and real innovation. Data architecture sits right at the center of that shift. The platforms you build here will be the foundation that AI systems run on, and you'll be developing the kind of AI fluency that makes engineers genuinely future-proof.

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What that looks like in practice:

• You'll work on projects with real stakes: AI platforms for clinical trials, healthcare data infrastructure, financial services
• You'll have the autonomy to make architectural decisions, not just implement someone else's
• You'll present ideas at internal ‘Shark Tank’ style pitches and Hackathons: if you want to build something, there's a path to doing it
• Our founder's guarantee: you’ll be a part of at least one ongoing project that genuinely interests and challenges you

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The hiring process – we mean business & move fast.

• Send us something real: your resume, GitHub, a short note. We're not fussy.
• Do a real-world challenge: we'll send a task relevant to the work, not a whiteboard puzzle.
• Talk to the people you'd work with: no HR screening theatre.
• Get a fast answer: yes or no, with a reason either way.