About the Role:
Tredence is building a new archetype of data engineer — one who combines deep data engineering skills with AI native way of working. This is not a build-and-hand-off role. As a Data Engineering Architect, you will work shoulder-to-shoulder with clients (internal and external), translating ambiguous requirements into architecturally sound, production-ready data platforms. You will own the full development arc: from whiteboard to working system.
You are AI-native by default, and this role is best suited to someone who has shipped data products where performance, reliability, and iteration speed are non-negotiable
What You Will Do:
- Build Data Engineering accelerators by owning end-to-end delivery of Lakehouse and data platform solutions, from architecture design through production go-live.
- Run technical demos, proof-of-concepts, and Flash Demo sprints — translating prototype fidelity into accurate delivery scopes.
- Architect and implement end-to-end data platform solutions on Snowflake, designing multi-layered data architectures — from raw ingestion through curated, consumption-ready layers — using modelling paradigms including Kimball, Inmon, and Data Vault, and leveraging Snowflake-native capabilities to replace traditional Lakehouse infrastructure
- Design and deploy data platforms across hybrid and multi-cloud environments (Azure, AWS, GCP), managing data assets with consistency and governance at scale.
- Build and customize the accelerators for performance, compute cost-efficiency, and operational reliability based on customer requirements
- Implement advanced security frameworks including ABAC, RBAC, and Microsoft Information Protection across platform layers.
- Automate governance and deployment workflows using API integrations and CI/CD pipelines; script platform-wide automation in Python, PowerShell, and Azure CLI.
- Use coding assistants (Cursor, CoCo, Claude) and agentic development frameworks as primary tools and not supplements throughout the delivery lifecycle.
- Operate within Tredence's Agentic Delivery Lifecycle (ADLC): scaffold, harden, stabilize, and ship in rapid iterative loops anchored to entity-level specs.
- Mentor Apprentice and Ninja-tier engineers through hands-on pairing, code reviews, and structured comprehension assessments
What You Bring:
EXPERIENCE
- 10+ years of hands-on data engineering experience, with a preference for time spent in a product company environment.
- Demonstrate hands-on proficiency across the Snowflake modern data stack — Snowpark for Python and Java-based data engineering natively within Snowflake, Cortex (CoCo) for in-platform LLM and ML inference without data egress, and Snowflake Open Catalog / Iceberg-backed Data Lake capabilities for open, governed, multi-engine data access
- Proven experience managing data assets across hybrid and multi-cloud architectures.
- Direct client-facing or customer-embedded delivery experience - comfortable owning requirements, demos, and escalations personally.
TECHNICAL DEPTH
- Hands-on expertise with the Data Governance stack: Data Map, Scans, Classifications, Lineage, Glossary, and Policies.
- Advanced scripting capability in Python, PowerShell, and Azure CLI for platform-wide automation.
- Strong command of Spark performance optimization — not just writing jobs, but tuning them for cost and reliability at scale.
- Solid understanding of ABAC, RBAC, MIP, and modern data security frameworks.
- Experience implementing metadata management, enterprise data catalogues, and global privacy compliance (GDPR, CCPA).
- Hands-on experience with API integrations and CI/CD pipelines for governance and deployment automation.
AI-NATIVE WORKING STYLE
- Demonstrably productive with AI coding assistants (Cursor, CoCo, Claude) - not experimentation, but daily professional use.
- Familiar with agentic software development frameworks and spec-driven engineering practices.
- Comfortable working in agent-orchestrated delivery environments where the unit of work is a data entity, not a ticket.
PEOPLE & COMMUNICATION
- Exceptional stakeholder management: you build trust quickly, handle ambiguity without escalating anxiety, and know when to escalate versus absorb.
- Able to explain complex technical trade-offs in plain language to non-technical business partners and C-level stakeholders.
- Track record of mentoring and technically growing junior and mid-level engineers in delivery contexts.
