Data Architect
Role Summary
Join Techtorch as a Data Architect, designing modern data architectures with a focus on AI and enterprise-scale analytics. Remote work available.
About the Organisation
TechTorch's Data Practice sits at the intersection of enterprise data and applied AI. We design and build AI-native systems that don't just analyze the past — they actively drive decisions. Our work spans data infrastructure and pipelines, intelligent automation, and full-stack AI applications across industries.
We work the way the best client-delivery teams now operate: small teams, deep ownership, no hand-offs at boundaries. We take problems from a client whiteboard to production, and we let AI do the heavy lifting wherever it earns its place.
Key Responsibilities
- Develop and maintain enterprise data architecture, including models, flow diagrams, and integration frameworks
- Define and enforce architecture standards, governance models, and documentation practices
- Design scalable, flexible solutions that align with modern data warehousing and cloud-native best practices
- Drive the design and implementation of data integration pipelines and APIs across systems
- Lead efforts in data modeling, database design, and architecture optimization
- Collaborate with data engineers, analysts, and developers to ensure architectural consistency and performance
- Mentor and provide technical direction to engineers on the Data Practice team, including full-stack engineers building applications on top of the data foundation
- Oversee data quality, lineage, and security strategies across the organization
- Provide guidance on big data technologies, ETL platforms, and modern cloud ecosystems
- Support strategic decisions through architectural reviews, proofs of concept, and solution roadmaps
Minimum Requirements
We’re looking for high-caliber professionals who meet the following criteria:
- 7+ years of experience in data architecture, data engineering, or related roles in complex environments
- Demonstrated experience designing robust technical solutions for clients or customers
- Experience serving as a technical lead - mentoring, coaching, and setting technical direction for data engineers, analysts, and other technical resources
- Strong background in data modeling (conceptual, logical, physical), warehousing (e.g., Snowflake, Redshift, BigQuery), and database design
- Familiarity with modern data transformation practices (e.g., dbt) and dimensional modeling patterns, including Slowly Changing Dimensions (SCD)
- Proficient in data modeling tools (e.g., ER/Studio, Erwin) and relational/NoSQL databases (e.g., SQL Server, Oracle, MongoDB)
- Fluent with AI coding agents (e.g., Claude Code, Cursor) as production accelerators, with the credibility to evaluate and coach engineers on how they use them
- Skilled in applying AI/ML to complex data challenges, including designing architectures for generative AI and RAG solutions, and automating data pipelines
- Experience with CRM and/or ERP ecosystems (e.g., Salesforce, NetSuite) and analyzing data to uncover quality issues, identify revenue leakage and/or drive operational efficiency
- Knowledge of modern cloud data stacks (e.g., Azure, AWS, GCP)
- Hands-on experience with ETL/integration tools (e.g., Talend, Informatica)
- Familiarity with building and/or designing reporting solutions for business stakeholders (e.g., Power BI, Tableau)
- Comfortable contributing to business development activities such as supporting proposals, scoping projects, and growing accounts
- Strategic mindset with a focus on business value, scalability, and performance
- Excellent communication skills and ability to influence cross-functional stakeholders
- Adaptable, collaborative, and continuously improving in a fast-paced delivery environment
Eligibility Criteria
Candidates should have a strong background in data architecture and engineering, with no explicit restrictions on eligibility stated.
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