Software Engineer, Data Engineering

About the Team Data is at the foundation of DoorDash success. The Data Engineering team builds database solutions for various use cases including reporting, product analytics, marketing optimization and financial reporting. By implementing pipelines, data structures, and data warehouse architectures; this team serves as the foundation for decision-making at DoorDash. About the Role DoorDash is looking for a Senior Data Engineer to be a technical powerhouse to help us scale our data infrastructure, automation and tools to meet growing business needs. This is a hybrid position and you must be located in Sunnyvale, San Francisco, or Seattle. You're excited about this opportunity because you will... Work with business partners and stakeholders to understand data requirements Work with engineering, product teams and 3rd parties to collect required data Design, develop and implement large scale, high volume, high performance data models and pipelines for Data Lake and Data Warehouse Develop and implement data quality checks, conduct QA and implement monitoring routines Improve the reliability and scalability of our ETL processes Manage a portfolio of data products that deliver high-quality, trustworthy data Help onboard and support other engineers as they join the team We're excited about you because... 5+ years of professional experience 3+ years experience working in data engineering, business intelligence, or a similar role Proficiency in programming languages such as Python/Java 3+ years of experience in ETL orchestration and workflow management tools like Airflow, Flink, Oozie and Azkaban using AWS/GCP Expert in Database fundamentals, SQL and distributed computing 3+ years of experience with the Distributed data/similar ecosystem (Spark, Hive, Druid, Presto) and streaming technologies such as Kafka/Flink. Experience working with Snowflake, Redshift, PostgreSQL and/or other DBMS platforms Excellent communication skills and experience working with technical and non-tec

About the Role The Data engineering team has been created keeping in mind the importance and criticality of data for the overall success of Chime. The mission of this group is to build scalable data artifacts and insights that enable accurate, governed, data-driven decision-making.. As a core member of the team, you will have the opportunity to build scalable data pipelines and frameworks. Additionally, you will have the unique opportunity to architect and build workflows that could potentially become de facto standards for the fintech industry. In this role, you can expect to Be a hands-on data engineer, building, scaling and optimizing ETL pipelines Design data schemas and scale them data for 10x data growth Ownership of all aspects of data - data quality, data governance, schema design, data quality and security Develop scalable data architecture and standardized data models that enable self-serve data insights using AI. Own the ETL workflows and make sure the pipeline meets data quality and availability requirements Work closely with partner teams, like Data Science, Analytics and DevOps To thrive in this role, you have 5+ years experience transforming data to governed and lucid datasets 5+ years of hands-on experience to build and deploy production-quality data pipelines 5+ years of experience working with stakeholders to provide business insights 5+ years of experience writing SQL, Spark, AWS Glue, EMR, Airflow, Python 3+ years of hands-on experience using any MPP database system like Snowflake, AWS Redshift or Teradata Track record of successful partnerships with Analytics, Data Science and DevOps teams Understanding of key metrics for data pipelines and has built solutions to provide visibility to partner teams Engineers are required to participate in on-call rotation. Being on call may include responding to incidents outside of regular working hours when necessary. #LI-Onsite #LI-WW1 Below is the base salary offered for t

Who We Are Notion is the collaborative AI workspace where teams and agents think together . We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. About the Role: Notion’s Data Foundations team builds and operates the batch and streaming infrastructure behind our product features, analytics, search, and AI experiences. We’re looking for a hands-on technical leader to shape the next generation of this platform as Notion serves larger customers, expands globally, and supports more data-intensive products. You’ll identify the highest-leverage problems, set direction, build and develop a high-performing team, and lead multi-quarter initiatives across our data lake, streaming, distributed-compute, governance, and reliability systems. You’ll stay close to critical technical decisions while creating clear ownership, growing engineers and technical leaders, and helping the team execute as one—partnering closely with Data Engineering, Data Product, Search, AI, Infrastructure, and Security. This role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person. We’re looking for someone who’s excited to work alongside the team during those days. What You

About the Role Chime’s Data Platform team builds the infrastructure every engineering and analytics team depends on - ingestion, transformation, quality, governance, and self-serve tooling across batch and streaming workloads. You’ll own core platform systems at scale, design the frameworks other teams build on, and set the technical standards for how data moves through Chime. High autonomy, real architectural trade-offs, production impact from day one. We’re hiring multiple engineers into this role at the senior level. In this role, you can expect to Design, build, and operate self-serve ETL/ELT frameworks supporting both batch and streaming workloads, with direct ownership of pipeline reliability, data quality SLAs, and schema evolution Drive the technical roadmap for our data platform - evaluate build-vs-buy decisions, define integration patterns, and set standards that engineering teams across the company adopt Partner with Product Engineering, Data Science, Analytics, and Marketing as a technical lead to design data contracts, onboard new data domains, and scale ingestion without sacrificing governance Architect and enforce data lineage, schema registry, and access controls across all domains - ensuring compliance with fintech regulatory requirements (SOX, PII handling) Own the platform - monitor, alert, debug, and resolve incidents across the data platform with the urgency expected of infrastructure that powers financial products Raise the technical bar of the team through code review, design reviews, and hands-on mentorship of junior and mid-level engineers Engineers are required to participate in on-call rotation. Being on call may include responding to incidents outside of regular working hours when necessary. To thrive in this role, you have 5+ years building, shipping, and operating data infrastructure in production - not just writing pipelines, but owning their reliability, performance, and cost at scale Strong system design skills - you’ve written desig

P-59 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. Modern data analysis employs sophisticated methods such as machine learning that go well beyond the roll-up and drill-down capabilities of traditional SQL query engines. As a software engineer on the Runtime team at Databricks, you will be building the next generation distributed data storage and processing systems that can outperform specialized SQL query engines in relational query performance, yet provide the expressiveness and programming abstractions to support diverse workloads ranging from ETL to data science. Below are some example projects: Apache Spark™ : Develop the de facto open source standard framework for big data. Data Plane Storage : Provide reliable and high performance services and client libraries for storing and accessing humongous amount of data on cloud storage backends, e.g., AWS S3, Azure Blob Store. Delta Lake : A storage management system that combines the scale and cost-efficiency of data lakes, the performance and reliability of a data warehouse, and the low latency of streaming. Its higher level abstractions and guarantees, including ACID transactions and time travel, drastically simplify the complexity of real-world data engineering architecture. Delta Pipelines : It's difficult to manage even a single data engineering pipeline. The goal of the Delta Pipelines project is to make it simple and poss

About the Team OpenAI’s mission is to ensure that artificial general intelligence (AGI) benefits all of humanity. A key part of achieving that mission is training models that deeply understand and reflect human preferences — the Human Data team is at the heart of that effort. The Human Data engineering team creates the systems that enable scalable, high-quality human feedback. These systems are essential to how OpenAI trains and improves its most advanced models. Engineers on this team collaborate closely with world-class researchers to bring alignment techniques to life — from experimental ideas to production-ready feedback loops. About the Role We’re looking for software engineers to join the Human Data team and build the platforms, prototypes, tools, and infrastructure that power how our AI models are trained, aligned, and evaluated. You’ll partner with researchers and cross-functional teams to bring alignment ideas to life, influence future model training, and shape how models interact with the real world. We’re looking for people who are excited by technical ownership, enjoy working across the stack, and are eager to solve ambiguous problems in a high-impact, fast-paced environment. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Build and maintain robust full-stack systems for feedback collection, data labeling, and evaluation pipelines, while maintaining high levels of security. Translate experimental alignment research into scalable production infrastructure, including inference and model training stacks. Design and iterate on user-facing tools and backend services to support high-quality data workflows Partner with researchers, engineers, and program leads to shape feedback loops and model interaction paradigms Drive infrastructure improvements that enable faster iteration and scaling across OpenAI’s frontier models, from internal r