Fractal

Design and architect enterprise-grade data platforms on AWS and Databricks for financial services. | Requires strong AWS and Databricks experience, data modeling, SQL, Python, Spark, and platform architecture skills. | Databricks Platform Architect Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner. Please visit Fractal | Intelligence for Imagination for more information about Fractal. Note: This position is not eligible for Immigration Sponsorship at this time. Role Overview We are seeking a Platform Architect for Financial Services who would be responsible for designing, governing, and scaling enterprise‑grade data models and foundation data architecture on AWS and Databricks. The role ensures that data is modeled, governed, and optimized to support analytical, operational, and AI/ML workloads. This architect collaborates with engineering, governance, and business teams to establish modeling best practices, ensure platform alignment, and enable scalable, high‑quality data platforms. Requirements Core Technical Expertise • Strong experience in conceptual, logical, and physical data modeling • Hands‑on experience with AWS data services (S3, Glue, Redshift, DynamoDB, Athena, EMR) and Databricks • Proficiency in SQL, Python, Spark, and modern data integration frameworks • Experience with dbt Core/Cloud and Data Vault 2.0 (including automate_dv) Architecture & Data Engineering Skills • Experience with data integration patterns and model ETL/ELT pipelines • Experience designing data provisioning layers for downstream consumption • Familiarity with modeling for AI/ML, including feature structuring and model‑ready datasets Platform & Cloud Skills • Understanding of Databricks Lakehouse modeling patterns (Delta Lake, Unity Catalog) • Experience optimizing models for performance, cost, and scalability • Experience implementing observability, monitoring, and data quality validation Collaboration & Leadership • Ability to work across engineering, governance, and business teams • Strong communication skills with ability to explain modeling decisions clearly • Experience working in hybrid onsite/offshore delivery models • Financial services experience preferred Roles & Responsibilities • Design and architect enterprise-grade data platforms using AWS services and Databricks Lakehouse Platform. • Define platform architecture for data ingestion, processing, storage, governance, observability, security, and cost optimization. • Build scalable and secure data lakehouse solutions using Databricks, Delta Lake, Unity Catalog, Spark, and AWS native services. • Design and implement cloud infrastructure using AWS services such as S3, IAM, VPC, Glue, Lambda, EMR, Redshift, Athena, KMS, CloudWatch, and Step Functions. • Lead platform setup, workspace configuration, cluster policies, access controls, networking, and environment segregation across dev, test, and production. • Establish best practices for Databricks workspace architecture, Unity Catalog governance, job orchestration, CI/CD, and data security. • Collaborate with data engineering, analytics, AI/ML, security, and infrastructure teams to deliver robust platform capabilities. • Define reusable platform patterns, reference architectures, automation templates, and deployment standards. • Drive implementation of Infrastructure as Code using tools such as Terraform, CloudFormation, or similar. • Implement CI/CD pipelines for Databricks notebooks, jobs, workflows, libraries, and infrastructure deployments. • Ensure platform compliance with enterprise security, data privacy, governance, audit, and regulatory requirements. • Optimize Databricks clusters, Spark workloads, storage design, and AWS resource usage for performance and cost efficiency. • Support migration and modernization of legacy data platforms to AWS and Databricks. • Provide technical leadership, architecture reviews, design guidance, and mentoring to engineering teams. • Work with stakeholders to translate business and technical requirements into scalable platform solutions. • Evaluate new AWS and Databricks features and recommend adoption where relevant. • Define monitoring, logging, alerting, incident response, and operational support processes for the platform. • Create technical documentation, architecture diagrams, runbooks, standards, and governance guidelines. Platform Architecture Support • Partner with platform architects to align data models with cloud‑native platform design • Support design of data ingestion, transformation, and provisioning patterns • Ensure models align with security, compliance, and access‑control requirements • Contribute to reference architectures and reusable modeling patterns Engineering Collaboration • Work closely with data engineers to ensure accurate implementation of models • Provide modeling guidance during solution design reviews • Support automation of modeling and metadata processes where possible Quality, Optimization & Governance • Ensure data models are optimized for cost, performance, and maintainability • Contribute to governance frameworks for data standards and lifecycle management • Support monitoring, observability, and quality validation for modeled datasets Pay The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $125,000 - $175,000. In addition, for the current performance period, you may be eligible for a discretionary bonus. Benefits As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company.In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides for 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take time needed for either sick time or vacation. Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Lead engineering teams to architect and deliver AI and data platforms for TMT clients with executive presence and embedded delivery. | Senior technical leader with 15-20 years in AI/data engineering, executive presence, client-facing skills, and experience in TMT verticals and embedded delivery models. | Fractal is a strategic AI partner to Fortune 500 companies, with a bold vision: to power every human decision in the enterprise. We believe the future belongs to organizations that combine human imagination with intelligent systems—and Fractalites are the ones building that future. As we scale our Technology, Media & Telecom (TMT) practice in the United States, we are looking for a senior, client-facing Head of Engineering to shape and deliver world-class Data & AI platforms for leading Technology, Media & Telecom organizations. This is not a back-office engineering role. This is a consulting-led, client-facing engineering leadership position for someone who is equally comfortable whiteboarding architecture with principal engineers, rolling up their sleeves with delivery teams, and advising CIOs, CTOs, and CDOs in the boardroom. Learn more at Fractal | Intelligence for Imagination. Note: This position is not eligible for Immigration Sponsorship at this time. About the Role This is a four-axis leadership role requiring technical depth, executive presence, team leadership, and embedded delivery. You'll work directly with top technical and functional leaders at some of the largest TMT companies in the world. As Head of Engineering for Fractal's Technology, Media & Telecom (TMT) vertical, you will personally shape the architecture of mission-critical AIML platforms, often in first-party tech stack, and develop/drive the team of ICs who bring them to life. Responsibilities Some engagements will look like a traditional advisory model. Others will look a lot more like Forward Deployed Engineering: your team embedded inside a client's engineering org, working within their first-party tech stack, shipping production code alongside their engineers, and earning influence through technical credibility, not org chart position. You will need to be in the room when the technology roadmap needs to change. When a business pivot, a new regulation, or a technology shift forces a rethink mid-execution, you are the person who picks up the marker, walks to the whiteboard, and redraws the architecture in real time, credibly, for the CTO, and Principal Engineering leaders simultaneously. Technical Depth (Hands-On Architecture) • Own AI/Data platform architecture decisions — from Lakehouse design and real-time streaming to MLOps, LLMOps, and AgentOps pipelines in production • Serve as the technical authority for Fractal's TMT engineering practice — defining standards, reviewing design, and holding the bar on reliability, scalability, and security • Translate ambiguous business problems into concrete, buildable platform architectures — and stay close enough to execution to know when something is not working • Drive the industrialization of GenAI: moving clients from proof-of-concept to enterprise-grade, governed, and observable AI systems Executive Presence & Live Architectural Thinking • Command the room with senior client leadership — CIOs, CTOs, CDOs, and their direct reports - as a peer, not a vendor • Whiteboard new architectural directions on the spot: when a business pivot, acquisition, regulatory shift, or technology breakthrough forces a mid-execution rethink, you synthesize it into a credible, buildable path forward live, in the room, without needing a week to prepare a deck • Translate between two worlds simultaneously: make the architecture legible to a CFO and rigorous enough to satisfy a principal engineer in the same session • Shape client roadmaps at the strategic level; identifying where the current plan is under-ambitious, over-engineered, or misaligned with emerging AI capabilities, and steering accordingly • Represent Fractal at the highest level of client relationship Team Leadership (Building & Driving Senior ICs) • Develop and lead a high-performing group of individual contributors. principally senior and staff engineers, ML engineers, and data platform engineers • Create the engineering culture: rigorous delivery standards, architectural thinking, and a bias toward elegant, production-grade solutions over quick fixes • Build leadership depth within the team, identifying principals who can own programs and grow into broader roles • Partner across Fractal's global AI and engineering Capability functions to staff programs strategically and raise capability across the TMT practice Forward-Deployed & Embedded Delivery • Lead and run FDE-style engagements where your team operates inside the client's engineering environment • Navigate and deliver within client-owned, first-party technology stacks: proprietary data platforms, internal ML infrastructure, custom orchestration systems, and bespoke toolchains that do not appear in any industry survey • Adapt quickly to non-standard environments, understanding a client's internal platform deeply enough to extend it, integrate into it, and earn the trust of their engineering staff • Balance the tension between what Fractal does best and what the client's stack demands, knowing when to bring pattern, when to adapt, and when to advocate for a better path • Set the standards for how Fractal operates in deeply embedded engagements: how we onboard, document, transfer knowledge, and leave clients stronger than we found them Candidate Profile Technical Qualifications TMT clients bring genuinely hard problems on both open and proprietary infrastructure. Expect to architect and oversee: • GenAI systems: RAG architectures, LLM fine-tuning pipelines, agentic workflow orchestration, and LLMOps observability • AI-powered products: personalization engines, churn prediction, content recommendation, and network fault detection • Client-proprietary ML infrastructure: internal feature stores, custom model serving layers, bespoke experiment tracking systems, and first-party orchestration frameworks • Cloud-native infrastructure across AWS, Azure, and GCP with enterprise-grade governance, security, and compliance baked in • Real-time and event driven data pipelines (e.g. network telemetry) • Modern Lakehouse platforms (Databricks, Snowflake, Delta Lake, Iceberg) at petabyte scale and proprietary data platform equivalents at leading tech-forward TMT organizations Non-technical Qualifications We are particularly interested in leaders from environments where engineering rigor, client accountability, executive presence, and AI depth all coexist including Forward Deployed Engineering, elite data/ML platform teams, and senior hyperscaler architecture practices. • 15–20 years of experience spanning AI/data engineering and technical leadership with clear evidence of owning architecture at scale • Deep hands-on experience deploying AI/ML/GenAI systems in production, in addition to advising on them • Demonstrated executive presence: you have walked into a CTO or CDO review, redrawn the architecture based on new constraints, and left the room with alignment • The ability to whiteboard fluently under pressure, synthesizing a team's in-flight work with a new business direction, making it rigorous enough for engineers and clear enough for executives, on the spot and without a rehearsal • Experience operating within client-owned or non-standard technology stacks - you have learned a proprietary system, earned trust from skeptical internal engineers, and delivered production-grade results inside someone else's infrastructure • A track record of leading senior engineers and building high-performance ML/engineering teams, including hiring, coaching, and developing principal-level ICs • Direct executive engagement experience - you have influenced CIO/CTO/CDO decisions and can hold your own in a room with technical and non-technical stakeholders at once • Strong cloud-native fluency across one or more hyperscalers, with genuine depth in data platform patterns (streaming, batch, Lakehouse, governance) Strong Preferences • Experience in TMT vertical — hi-tech, telco, media platforms, streaming infrastructure, ad tech, or content delivery at scale • Prior work in FDE-style or embedded delivery models where your team shipped inside a client codebase and was evaluated by their engineering standards, not just deliverable milestones • Comfort with the ambiguity of 1P stack environments: you have debugged undocumented internal tools, extended proprietary frameworks, and figured out how to make external expertise land inside a closed ecosystem • A personal reputation for architectural clarity: the person colleagues call when a problem needs to be drawn, not just describe • Contributions to the ML/AI community: open source, publications, conference talks, or influential architectural patterns Who Thrives Here The Fractalite mindset is curious, rigorous, and impact driven. You will thrive in this role if you: • Enjoy being client-facing and accountable for outcomes. • Are comfortable navigating ambiguity, scale, and complex stakeholder environments. • Believe great platforms come from strong engineering culture plus disciplined execution. • See AI not as a novelty, but as a core enterprise capability that must be engineered responsibly. Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.