Responsibilities: - Translate business needs and architectural guidance into detailed designs, data contracts, and implementation plans that break down large initiatives into actionable engineering tasks with reliable estimates - Create detailed pipeline designs covering schemas, transformations, partitioning, DLT configurations, orchestration, error handling, and observability that align with the platform architecture through close collaboration with the Data Architect - Lead implementation and guide junior engineers on design, coding standards, and best practices - Develop metadata-driven and configuration-driven pipeline patterns that reduce custom code and improve consistency - Make technical decisions that ensure reliability, performance, maintainability, and scalability. Ensure production readiness with monitoring, lineage, alerting, observability, CI/CD and documentation - Define and enforce engineering design patterns, coding standards, testing practices, and operational best practices - Evaluate and incorporate new technologies and Databricks capabilities that improve reliability, performance, or developer productivity - Validate new technologies with the Data Architect and operationalize them through documentation, examples, and enablement - Implement automated data quality checks, rule enforcement, and exception handling - Production support of both an existing and new platform including optimization of jobs, incident tracking and other analysis required for production - Lead resolution of complex production issues and deliver durable root cause fixes - Maintain SLAs for reliability, recovery, idempotency, performance, and cost efficiency - Mentor Level 2–3 engineers through pairing, design guidance, code reviews, and technical coaching Basic Skills: • Bachelor’s degree preferred; equivalent experience accepted • 10+ years in data engineering (12+ without a degree) • 4+ years building production-grade batch/streaming pipelines using PySpark, Spark Structured Streaming, Python, and SQL • Proven experience with data governance, schema evolution, data lineage, and secure access patterns • Proven 2 years’ experience with maintaining and sustaining data pipelines Preferred Skills: • 3+ years hands-on with Databricks (Delta Lake, DLT, Unity Catalog, workflow jobs) within the last 6 years • Experience building metadata-driven or configuration-driven pipelines • Experience with data quality frameworks (DQX, Great Expectations, or equivalent) • Experience with observability, metrics and query performance analysis • Strong Spark optimization Work Conditions: • Team collaborative hours between 8am to 4pm EST • Corporate office/lab environment • Ability to travel 10% of the time

• Design and develop high‑performance data converters for multi‑sensor autonomous‑driving data (camera, lidar, radar), ensuring accurate time alignment and robust handling of raw sensor logs. • Design, build, and optimize large‑scale ingestion and transformation pipelines (ETL/ELT) capable of processing petabyte‑scale autonomous‑driving sensor data, and automate them for reliable, production‑grade deployment. • Work with data formats such as ROS bags, MCAP, and custom binary encodings; establish standards for schema evolution and metadata integrity. • Implement automated data validation, quality checks, and lineage tracking to ensure reliability of production datasets. • Collaborate closely with ML, annotation, simulation, and perception teams to ensure cross‑team ownership of data products and deliver datasets that are consistent, semantically correct, and ready for downstream consumption. • Proactively assess current capabilities to identify areas for improvement proposing solutions that align with core strategy and operation.

• Design, develop, and maintain scalable real-time data pipelines to support analytics and operational use cases. • Own and operate cloud-based infrastructure, following a 'you build it, you run it' model to ensure reliability, performance, and scalability. • Develop and manage data products, collaborating with a diverse set of stakeholders including product managers, analysts, and engineering teams. • Mentor and support other engineers, contributing to a collaborative and high-performing engineering culture. • Build a deep understanding of a complex business domain and work effectively across teams with multiple dependencies. • Own features end-to-end, from design and implementation through deployment, monitoring, and ongoing improvement.

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! What you'll do: This is a unique opportunity to help us build robust and scalable systems and services that handle data workloads at Haus. The Data Platform includes systems that power the Haus product and are at the heart of what we do. The ideal candidate is somebody who loves working with data, is a great software engineer, and an excellent communicator. You will design, scale, and maintain the systems that support the Haus platform and enable data-driven decision-making at Haus. Responsibilities - Build data solutions on top of Google Cloud Platform (GCP) using Python and other languages as appropriate. - Contribute to the technical direction and development of Haus’ data services, infrastructure and tools. - Create and maintain scalable data pipelines and foundational datasets to support analytics, modeling, experimentation, and product/business needs. - Develop and support data quality checks at scale, implementing alerting and anomaly detection as necessary - Create dashboards and reports to support business objectives and enable data-driven decision making - Partner with data scientists, engineers, and product teams to accomplish all of the above! Qualifications - 4-7+ years of experience as a Software Engineer. - Experience building and deploying large-scale distributed systems in Python or Go: it makes you uncomfortable deploying something for end-users without thinking about things like performance metrics, monitors and dashboards, graceful degradation and feature flags. - Experience with cloud infrastructure (Google Cloud, AWS, etc). - Experience building ETL/ELT data pipelines - batch and realtime - Experience working with SQL and NoSQL databases. Bonus points - Earlier stage startup experience. - Experience with data warehouses: Snowflake, BigQuery, RedShift. - Experience with data workflows and tools: dbt, dagster. - Experience with Third party data APIs integrations - Experience with build systems and infrastructure management tooling like Terraform. - BS/MS/PhD in Computer Science, Applied Mathematics or a related field. What we offer 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 are remote-friendly however candidates based in commutable distance (50 mi?) to SF, Seattle and NYC will be prioritized as we value frequent in-person collaboration opportunities at Haus. 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.