About Data Society Group At Data Society Group, we provide the highest quality, leading-edge, industry-tailored data and AI training and solutions for Fortune 1,000 companies and federal, state, and local governmental organizations. We partner with our clients to educate, equip, and empower their workforces with the skills they need to achieve their goals and expand their impact. We are empowering the workforces of the future, supporting engineers and scientists to train up on the most complex AI solutions and Machine Learning skills. Role Overview We are seeking a capable and resourceful Data Engineer with expertise in cloud-based text-focused AI systems to join our technology and solutions team. In this role, you will be a key individual contributor, applying your expertise to build robust, scalable, and complex data and AI solutions for our external clients. You will work within a cross-functional team, collaborating closely with UX Designers, Engineers, and Project Managers to translate client requirements into high-quality technical deliverables. Responsibilities Design, build, and maintain scalable data pipelines for structured and unstructured data ingestion, transformation, and processing. Architect, build, and deploy LLM-based solutions on cloud platforms, including prompt pipelines, orchestration layers, embeddings, vector databases, and evaluation workflows. Design and implement RAG systems end-to-end including document ingestion, chunking/embedding, indexing, retrieval, grounding, model integration. Architect and enforce data models, governance, cataloging and schema design to support both analytics and AI workloads. Build and optimize cloud-native data architectures to support compute, storage, and orchestration for high-throughput, production-grade AI workloads. Implement reliable and efficient ETL patterns, leveraging best practices for data quality, lineage, versioning, and cataloging. Instrument observability and monitoring for data pipelines, including latency, error rates, and schema drift, with alerting and automated remediation where possible. Implement monitoring, observability, and performance optimization for data and AI systems. Operate effectively within Agile workflows, contribute to sprint planning, estimations, backlog refinement and continuous improvement. Work closely with clients to gather requirements, provide technical guidance and present solutions and implementation plans. Communicate complex technical information to both technical and non-technical stakeholders. Work cross-functionally with UX, engineering, and PM teams to deliver client-facing solutions. Translate complex technical needs into clear development requirements and implementation plans. Stay current with emerging technologies and recommend improvements to our engineering practices, architecture patterns, and cloud ecosystem. Qualifications Hands-on experience deploying LLM-based applications, including RAG or similar retrieval systems. Proven experience deploying systems on AWS or Azure (AWS preferred). Strong understanding of embeddings, chunking strategies, retrieval optimization, and evaluation. 5+ years of data and analytics engineering in cloud environments. Expertise in SQL, Python, and schema design with experience in data cataloging and governance tools. Demonstrated experience building robust and maintainable data architectures, including real-time or steaming pipelines. Experience working in Agile / Scrum development processes. Excellent communication skills and ability to work cross-functionally with non-technical teams. Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice. This position will be remote in the US though based out of the Washington, DC area with travel to client sites in DC if needed.

Spotify is looking for a Backend Engineer to join our Data Infrastructure engineering organization. You will forge new ground in how we help teams at Spotify build analytics for internal and Music industry facing tools. Our platform enables capabilities that include showing artists how many streams their latest release has to informing internal teams about their cloud resource usage. As a part of a platform team, you will help exemplify, measure and raise the reliability of data infrastructure of squads across different verticals within Spotify. You’ll work closely with engineers to provide OLAP capabilities to build dynamic, reliable data visualizations and share responsibility with them in diagnosing, resolving, and preventing production issues. We are strong believers in engineering teams taking operational responsibility for their products and work hard to support them in this. What You'll Do Build, operate, and evolve data analytics platform that includes backend services as well as OLAP data store (Druid) for teams building analytics across Spotify. Build internal tooling, libraries, and services that streamline integration patterns with our analytics platform. Advocate for best practices in service design, data modeling, schema evolution, and contract testing to ensure long-term maintainability. Work in an autonomous, mult-functional environment and collaborate with squads across Spotify to continuously iterate and deliver on new product objectives. Who You Are

Dandy is transforming the massive and antiquated dental industry—an industry worth over $200B. Backed by some of the world’s leading venture capital firms, we’re on an ambitious mission to simplify and modernize every function of the dental practice through technology. As we expand our reach globally, Dandy is building the operating system for dental offices around the world—empowering clinicians and their teams with technology, innovation, and world-class support to achieve more for their practices, their people, and their patients. Lead the Architecture of Dandy’s Business Logic and Data Products At Dandy, we are revolutionizing the dental industry, and data is the engine of our growth. We are looking for a Manager of Data Engineering to build and lead a high-performing team focused on the "T" in ELT- modeling, transformation, and data product ownership. This is a strategic leadership role that pivots away from platform infrastructure (managed by a separate team) to focus entirely on Data Modeling, Semantic Architecture, and Technical Rigor. You will lead the team that bridges the gap between raw software application data and the insights that drive our business. You will be the guardian of our Single Source of Truth, ensuring that our Analysts and Data Scientists are empowered with fast, reliable, and performance-optimized data products. The Mission Own the Data Products: You aren't just moving data; you are building durable, high-performance data assets that the business relies on. Empower the Consumers: Your team is the force multiplier for Data Science and Analytics. You remove the friction of data cleaning and query optimization so they can focus on insights. Enforce Technical Rigor: You ensure that our data models are not just accurate, but performant, scalable, and built with software engineering best practices. What You’ll Do Build & Lead the Data Engineering Team: Define the vision for a team that sits at the intersection of engineering and analytics. You will hire, mentor, and define career paths for engineers who specialize in high-complexity modeling and transformation. Architect the Semantic Layer: Move beyond simple replication. You will design domain-driven data models that crystallize business concepts into a trusted, centralized semantic layer. Drive Technical Rigor & Performance: You will own the health of our data warehouse. This means optimizing SQL query performance, designing efficient table structures, and ensuring our pipelines are cost-effective and lightning-fast. Proactive "Upstream" Engineering: Stop fixing broken data downstream. You will partner with Software Engineering teams before code is shipped to implement Data Contracts and ensure long-term data success is baked into the application design. Accelerate Data Velocity: Your team will build the "paved road" for data access. You will create modular, reusable data components that allow Analysts and Data Scientists to answer new questions in hours, not weeks. Treat Data as a Product: Implement rigorous SLAs (Service Level Agreements) for data availability and quality. You will ensure that the data products we build are monitored, tested, and reliable. What We’re Looking For Modeling & Architecture Expertise: You have deep experience in dimensional modeling (Kimball), data mart design, and complex SQL logic. You know how to translate messy real-world processes into clean, query-efficient structures. Leadership Experience: 3+ years managing Data Engineers or Analytics Engineers. You know how to balance the technical growth of your team with the delivery of business value. Technical Excellence (SQL & dbt): You are an expert in the modern data stack (dbt, Snowflake/BigQuery, Airflow/Dagster). You understand how to write code that is not only correct but highly performant at scale. Collaborative Stakeholder Management: You can translate technical constraints to business stakeholders and advocate for data needs with software engineering leaders. You are comfortable negotiating data contracts and preventing technical debt. Focus on Reliability: You are obsessed with testing. You implement robust CI/CD pipelines for data, ensuring that no bad code breaks the build and no bad data reaches the CEO’s dashboard. Strategic Vision: You can see the big picture. You are building a data foundation that will support Dandy not just today, but through our next phase of hyper-growth. Why Dandy? Competitive Compensation: Salary, benefits, and performance-based incentives. Remote-first Culture: Work from anywhere within 3 hours of NYC time zone Leadership Opportunity: Play a pivotal role in shaping the future of our centralized data engineering function. Innovative Company: Help build the infrastructure that powers the revolution of the dental industry. Req ID: J-392 For full-time positions, Dandy offers a wide range of best-in-class, comprehensive, and inclusive benefits tailored to each country where we operate. Our local benefits packages typically include healthcare, dental, mental health support, parental planning resources, retirement savings options, and generous paid time off—ensuring our team members are supported no matter where they live and work. Dandy is proud to be an equal-opportunity employer. We are committed to building a diverse and inclusive culture that celebrates authenticity to win as one. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, citizenship or immigration status, or any other legally protected characteristics. Dandy also fully complies with the Americans with Disabilities Act (ADA). We are dedicated to embracing challenges and creating an accessible, inclusive workplace for all individuals. If you require any accommodations for your interview or have any questions beforehand, rest assured that we will do everything we can to meet your needs. Visit Dandy Careers for more! Data Privacy Notice: By submitting your application, you consent to Dandy collecting, storing, and processing your personal information for recruitment purposes in accordance with our Privacy Policy and GDPR regulations. You have the right to access, rectify, or request the deletion of your data at any time by contacting Privacy Requests .

• Lead the design and implementation of shared, reusable data models, defining shared fact tables, conformed dimensions, and a semantic/metrics layer that serves as the single source of truth across analytics functions • Drive standardization of data engineering practices across ADE and functional analytics teams, including pipeline patterns, CI/CD workflows, naming conventions, and data modeling standards • Partner with Data Infrastructure to modernize orchestration, improve pipeline decomposition, and establish secure dev/test environments with production data access • Architect and implement a shift-left data governance strategy, working with upstream data producers to establish data contracts, SLOs, and code-enforced quality gates that catch issues before production • Collaborate with Data Science leads and Product Management to translate metric definitions into reliable, certified data pipelines that power executive dashboards, WBR reporting, and growth measurement • Reduce operational burden by improving pipeline granularity, observability, and failure recovery, establishing runbooks and alerting standards that make on-call sustainable • Evaluate and integrate AI-native tooling into the data development lifecycle, enabling conversational data exploration with guardrails and AI-assisted pipeline development