Techtorch - Data Architect

Remote - U.S.+ Equity1mo ago

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 • Our Values • Our values reflect the DNA of the private equity-backed companies we serve — focused on speed, ownership, accountability, and results: • Client First – We focus relentlessly on delivering outcomes that create value for our clients • Client First • We, Not Me – We win together. Collaboration drives transformation at scale • We, Not Me • Get Stuff Done – We execute with speed and precision — because in PE, time matters • Get Stuff Done • AI First – We embed AI at the core, enabling scalable, high-leverage solutions • AI First • Own It – We take accountability for results, delivering on what we promise • Own It • Agile Mindset – We adapt quickly and proactively seek better ways to move forward • Agile Mindset

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

Benefits

• Flexible, remote-first work environment with high-performance expectations and autonomy. • Semi-annual team offsites — we come together in person at least twice a year to connect, recharge, and do the work that's better face-to-face. • A team that takes AI tooling seriously and expects you to integrate it into your work. • High-autonomy, high-ownership work across the full arc of real client problems. • Access to the full modern data and AI stack — no one-tool shops. • Exposure to top-tier private equity firms and their portfolio companies.