Role Description We are looking for a Data Engineer to join our team. This role is fully remote and offers a direct long-term contract. - Location: Remote - Contract Type: Direct Contract - Long term - Payment: USD 5000 Qualifications - Strong experience with Python - Strong Software Engineering fundamentals like SOLID principles - Experience with containerized deployments in cloud environments (Docker, Kubernetes, EKS, etc.) - Experience working with varied data types and building robust data pipelines (nice to have) - Team player with a collaborative mindset - Comfortable in a dynamic startup environment - Experience in building and maintaining data infrastructure and pipelines in production - Growth mindset focused on continuous learning - Excited to learn about Machine Learning (nice to have) Requirements - Enjoy working on a team where code and software engineering processes are communal property - Dedicated to finding the right way to build software and continually improve - Excited about learning new things every day Benefits - Flexible work - work from home or use the Brooklyn office at your convenience - Project day - One day per month spent on whatever you think is important - Fitness stipend, commuter benefits - Business English language courses for employees in Argentina - Travel to team events and in-person all-hands - Technical books, VPN Access, high-end work computer, etc. Company Description We are a continuous multifactor authentication company that monitors biometric behavior to provide zero-interruption MFA. Our team consists of PhD machine learning experts and experienced professionals in enterprise applications and cybersecurity. - We use passive biometrics and machine learning to automate user identity - Our mission is to fundamentally change secure human-computer interaction - We launched with the United States Department of Defense as our first customer - We are a young, hungry, and well-funded company, offering a unique opportunity to join a driven engineering team

• Design, develop, and manage scalable data warehouse and data lake systems, both on-premises and in the cloud • Implement and maintain data modeling frameworks using UML and other design tools • Build and maintain ETL/ELT data pipelines to integrate diverse data sources • Ensure real-time and batch data ingestion, transformation, and loading • Ensure data accessibility, performance, and usability by optimizing query performance and addressing latency issues • Build and optimize data validation processes and analytical tools to support data-driven decision-making • Develop data governance methodologies, including data glossary, security protocols, and access management • Ensure compliance with internal and external data standards and regulations • Collaborate with cross-functional teams (data science, business intelligence, product management) to understand business needs and translate them into technical specifications • Provide technical guidance and mentorship to junior data engineers • Design and implement monitoring and alerting systems to ensure data quality, system stability, and optimal performance • Proactively address technical challenges and system limitations to prevent downtime

Role Description We are seeking a Senior Data Engineer to design, develop, and scale the data platform that powers analytics, business intelligence, machine learning, and customer-facing products across our organization. This role will focus on building reliable, high-performance data pipelines and modern data architectures that enable trusted, accessible, and actionable data. The ideal candidate combines strong software engineering skills with deep expertise in cloud data platforms and distributed data processing. This individual will partner with Product, Engineering, Business Intelligence, Data Science, and business stakeholders to deliver scalable data solutions that support operational, analytical, and AI-driven initiatives. - Design, build, and maintain scalable data pipelines and ELT/ETL workflows. - Develop and optimize data models and enterprise data structures that support reporting, analytics, machine learning, and operational applications. - Leverage Snowflake and modern cloud data platforms to ingest, transform, and optimize large datasets. - Partner with Business Intelligence and Data Science teams to deliver trusted, production-ready datasets. - Design and implement data quality, validation, and monitoring processes. - Support both batch and near real-time data integration requirements. - Develop APIs and data services that enable internal and customer-facing applications. - Optimize data pipeline performance, scalability, and cost efficiency. - Document data architectures, pipeline processes, and engineering standards. - Evaluate emerging technologies and recommend improvements to the organization's data platform. - Mentor team members and help establish engineering best practices. Qualifications - 7+ years of experience in Data Engineering, Software Engineering, or a related field. - Strong SQL skills and extensive experience working with modern cloud data platforms, preferably Snowflake. - Strong proficiency with Python and data engineering development practices. - Experience designing and maintaining large-scale ELT/ETL pipelines. - Strong understanding of data modeling, data warehousing, and distributed data architectures. - Experience integrating structured and semi-structured data from multiple internal and external sources. - Familiarity with data orchestration and transformation frameworks. - Strong troubleshooting and performance optimization skills. - Excellent communication and stakeholder management skills. - Ability to work independently while managing multiple initiatives. Preferred Qualifications - Automotive industry data experience, including vehicle, dealership, CRM, inventory, consumer, telematics, finance, aftermarket, or mobility datasets. - Experience with dbt and modern analytics engineering workflows. - Experience with streaming technologies and event-driven architectures. - Experience supporting Business Intelligence, Data Science, and AI initiatives. - Familiarity with machine learning data pipelines and feature engineering concepts. - Experience working in a remote-first environment. Requirements - Compensation commensurate on experience. - Participation in company benefit offerings include medical, dental, vision, unlimited paid leave, 401(k) matching, wellness, and more. Next Steps - If you meet the requirements, and are interested in applying for this role, please complete the online application, be sure to include a current resume and contact information. - Local and remote candidates (living within Eastern or Central Time Zone) will be considered. - No relocation assistance provided for this position. - NO PHONE CALLS PLEASE. Company Description Team Velocity is a full-service marketing agency serving the automotive industry, providing integrated marketing solutions to OEMs and dealerships nationwide. We leverage our proprietary Apollo™ technology platform to predict consumer behavior, personalize marketing campaigns, and help dealerships drive more sales and service revenue. Our team members are driven, creative, and collaborative, enjoying a unique culture where innovation and client success are paramount. Join us in revolutionizing automotive marketing and technology through powerful, data-driven insights, continuous improvement, and an unwavering commitment to reliability.

Title: Staff Engineer - Data Platform Location: San Francisco United States Location San Francisco, CA; Seattle, WA Employment Type Full time Location Type Hybrid Department Engineering Compensation - $240K – $260K Salary ranges are determined by role and level, and within the range individual pay is determined by additional factors including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in this job posting reflect the base salary only, and do not include equity or benefits. Job Description: About Haus Haus is the incrementality platform leading brands trust to optimize billions in ad spend worldwide. Using frontier causal inference-based econometric models to run experiments, we help brands measure the business impact of marketing, pricing, and promotions with scientific precision. Over $360B is spent annually on paid advertising in the US alone, and the famous quote "half the money I spend on advertising is wasted; the trouble is I don't know which half" still rings true. Haus helps marketers identify which half, and reallocate it to maximize growth. With a founding team of former product managers, economists, and engineers from Google, Netflix, Meta, and Amazon, we make high-quality decision science, incrementality testing, and causal marketing mix modeling accessible to businesses of all sizes-automating the heavy lifting of experiment design, data processing, and insights generation. Haus works with leading brands like FanDuel, Sonos, and Dr. Squatch, delivering ROI gains as high as 30x. Haus is well-capitalized and backed by top-tier VCs, including Insight Partners, Baseline Ventures, Haystack, and others. We're honored that Haus has once again been recognized by LinkedIn as a 2025 Top Startup! The Role Haus's data engineering team powers the entire incrementality platform - every causal experiment, every marketing mix model, every dollar of ad spend we help our customers reallocate runs on the pipelines this team builds. We are looking for a Staff Software Engineer to set the technical direction for how Haus ingests data from ad networks, customer warehouses, and partner tools, and how we normalize it into a clean, trustworthy foundation for our data science research and customer-facing products. You will be the senior-most IC on a 6-10 person team, partnering directly with engineering leadership, data science, and product teams to make Haus's data platform a durable competitive advantage. What you'll do - Be the tech-lead and architect for Haus's data ingestion and normalization platform - ad network APIs (Google, Meta, TikTok, Amazon, etc.), Fivetran connectors, and customer warehouses (Snowflake, BigQuery) - balancing throughput, cost, and reliability. - Design and lead implementation of high-leverage systems: schema evolution, data contracts, DQ frameworks, idempotent backfills, lineage, time-travel, data reproducibility and pipeline observability. - Drive architectural decisions in our GCP / BigQuery / dbt stack - build vs. buy, what to standardize, what to deprecate - and write the design docs that align Engineering, DS, and Product teams. - Raise the engineering bar through code review, design review, and mentorship; level up Senior engineers and unblock the team on the hardest problems. - Partner with data science to translate fuzzy modeling and research needs into pipeline contracts and SLAs that downstream teams can trust. - Own incident response and post-mortems for critical pipeline failures; turn one-off fires into systemic fixes. - Drive design and implementation of AI (Agentic) workflows for data quality and analytics - Influence the broader engineering org's data strategy. Qualifications - 8-10+ years of software engineering experience, with at least 4 years building production data platforms at meaningful scale (terabytes/day, hundreds of pipelines, or comparable). - Track record of Staff-level technical leadership: setting direction across multiple workstreams, writing design docs others build from, and mentoring senior engineers. - Deep expertise in Python and SQL/dbt, with strong fluency in a modern orchestrator (Dagster, Airflow, Temporal, etc) and a cloud data warehouse (BigQuery, Snowflake, etc). - Demonstrated ownership of a non-trivial data platform - schema design, schema evolution, data quality, lineage, cost, and reliability - not just writing pipelines, but designing the system the pipelines live in. - Strong product judgment - comfortable working with DS, ML, or analytics consumers and translating their needs into clean data contracts. - Excellent written and verbal communication; able to defend technical decisions to engineering, product, and exec stakeholders. Bonus Points - Background contributing to or maintaining open-source data tooling/frameworks (Apache Spark, Apache Beam, Apache Iceberg). - Experience building AI Agents in a data platform setting. What We Offer: R&D Roles: We're a high-performance, low-ego team operating in a fast-moving environment. We care deeply about our customers and expect everyone to take full ownership of their work - this is a place where high expectations fuel even higher growth. If you thrive in ambiguity, take pride in raising the bar, and want to work alongside top-tier peers who challenge and support you, you'll find unmatched opportunities here. If you're looking for predictability or rigid structure or you prefer order-taking to go-getting, we're probably not the right fit - and that's okay. We work in small, mission-driven teams that prioritize inclusion, collaboration, and growth over hierarchy or red tape. Some of our benefits include: - Flexible PTO - take time when you need it! - Equity - Startup environment with part-ownership in our successes - Top of the line health, dental, and vision insurance - multiple plan options so you can pick what fits you best - WFH stipend to support the set up you need to be productive - Events & Offsites - opportunities to connect and celebrate in real life! - Free Lunch - Grab a bite on us when you choose to work from the office (hub locations include SF, NYC and Seattle) - New Parent Leave - take time to welcome your newest Hausmate We value in-person collaboration at Haus and give preference to candidates within commuting distance of our offices in San Francisco, Seattle, and New York City. Haus is an equal opportunity employer. We make hiring decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected status. We believe diverse perspectives make us stronger and are committed to an inclusive culture where everyone feels seen, heard, and empowered to contribute. Bring your authentic self - we would love to hear from you.