• Design, build, and optimize high-volume, high-performance ELT pipelines for centralized data warehousing • Collaborate with Product Managers, ML Engineers, Data Scientists, and DevOps to define and enforce a reliable, scalable, and secure data platform architecture • Ensure adherence to data warehousing standards, data quality best practices, and metadata management processes • Take ownership of key data warehouse components and drive improvements in performance and reliability • Conduct architecture reviews and participate in system design discussions to provide technical leadership • Mentor and guide junior engineers, fostering a culture of quality and continuous learning

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next. A culture of experimentation enables Netflix to continuously evolve and improve our products, delivering more joy to existing members and attracting new members from around the globe. Because experimentation is so pervasive at Netflix, we continually enable new capabilities and onboard new initiatives to the platform with close cross-functional collaboration with our Software Engineering, Data Engineering, and Data Science partners. At the nerve center of experimentation at Netflix is our internal, Netflix-wide, Experimentation Platform which accelerates innovation by enabling teams to rapidly learn from and act on their ideas. We are looking to add a data engineer to the team to improve the speed, reliability, and structure of our data pipelines that process billions of new events per day. These data pipelines are the foundation on which all experiment analysis at Netflix is built. Responsibilities - Work closely with data science and software engineering teammates to act as a strategic thought partner, solve emerging data needs, and ensure that our platform continues to deliver trustworthy and useful analysis results - Work closely with data engineering and data science partners to set and improve the standards for experiment analysis across Netflix, e.g. via better data contracts and data health - Design, build, operate, and maintain Spark data pipelines that process billions of allocation events per day and expose a silver layer in Iceberg - Develop new tooling and pipeline automation to meet emerging experimentation-related business needs Qualifications - Strong proficiency with technologies like Spark or Flink, comfort working with large scale datasets - Strong proficiency in data modeling, processing, automated testing, health, observability, and debugging - Fluency in at least one SQL dialect and proficiency in at least one programming language (e.g. Java, Scala, Python) - Excellent communication and collaboration skills - Ability to navigate ambiguous problem statements and solve for cohesive platform data solutions Curious to learn more about our experimentation culture at Netflix? Read up on experimentation culture at Netflix and browse the Netflix Research page. Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $388,000.00 - $558,000.00. This compensation range will vary based on location. Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here. Netflix is a unique culture and environment. Learn more here. Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner. We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service. Job is open for no less than 7 days and will be removed when the position is filled.

At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice. Staff Data Engineer YOUR TASKS AND RESPONSIBILITIES The primary responsibilities of this role, Staff Data Engineer are to: A staff data engineer designs and leads the implementation of data flows to connect operational systems, data for analytics and business intelligence (BI) systems. Individuals in this role will: - Recognize opportunities to reuse existing data flows; - Lead the build of data streaming systems; - Optimize the code to ensure processes perform optimally; - Lead work on database management; Communicating Between Technical and Non-Technical Colleagues - Communicate effectively with technical and non-technical stakeholders; - Support and host discussions within a multidisciplinary team, with potentially difficult dynamics; - Be an advocate for the team externally, and can manage differing perspectives. Data Analysis and Synthesis - Undertake data profiling and source system analysis; - Present clear insights to colleagues to support the end use of the data. Data Development Process - Design, build and test data products that are complex or large scale; - Build teams to complete data integration services. Data Innovation - Understand the impact on the organization of emerging trends in data tools, analysis techniques and data usage; Data Integration Design - Select and implement the appropriate technologies to deliver resilient, scalable and future-proofed data solutions and integration pipelines; Data Modeling - Produce relevant data models across multiple subject areas; - Explain which models to use for which purpose; - Understand industry-recognized data modelling patterns and standards, and when to apply them; - Compare and align different data models. Metadata Management - Design an appropriate metadata repository and present changes to existing metadata repositories; - Understand a range of tools for storing and working with metadata; - Provide oversight and advice to more inexperienced members of the team. Problem Resolution - Respond to problems in databases, data processes, data products and services as they occur; - Initiate actions, monitor services and identify trends to resolve problems; - Determine the appropriate remedy and assist with its implementation, and with preventative measures. Programming and Build - Use agreed standards and tools to design, code, test, correct and document moderate-to-complex programs and scripts from agreed specifications and subsequent iterations; - Collaborate with others to review specifications where appropriate. Technical Understanding - Understand the core technical concepts related to the role, and apply them with guidance; Testing - Review requirements and specifications, and define test conditions; - Identify issues and risks associated with work; - Analyze and report test activities and results. WHO YOU ARE Bayer seeks an incumbent who possesses the following: Required Qualifications: - Proficiency in programming language such as Python or Java; - Experience with Big Data technologies such as Hadoop, Spark, and Kafka - Familiarity with ETL processes and tools;; - Knowledge of SQL and NoSQL databases; - Strong understanding of relational databases; - Experience with data warehousing solutions; - Proficiency with cloud platforms; - Expertise in data modeling and design; - Experience in designing and building scalable data pipelines; - Experience with RESTful APIs and data integration. Preferred Qualifications: - Relevant certifications (e.g., GCP Certified, AWS Certified, Azure Certified); - Bachelor’s degree in Computer Science, Data Engineering, Information Technology, or a related field; - Strong analytical and communication skills; - Ability to work collaboratively in a team environment; - High level of accuracy and attention to detail. There are multiple openings for this position. This posting will be available for application until at least 4/20/2026. Employees can expect to be paid a salary between $114,400 to $171,600. Additional compensation may include a bonus or commission (if relevant). Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc.. This salary range is merely an estimate and may vary based on an applicant’s location, market data/ranges, an applicant’s skills and prior relevant experience, certain degrees and certifications, and other relevant factors. YOUR APPLICATION Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Health for all, Hunger for none, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer. To all recruitment agencies: Bayer does not accept unsolicited third party resumes. Bayer is an Equal Opportunity Employer/Disabled/Veterans Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below. Equal Opportunity Employer Statement: Notice for U.S. Visitors: All information on this site is subject to compliance with local rule and regulations as they may vary from time to time and across different geographies, including, without limitation, U.S. Executive Orders. Bayer is an E-Verify Employer. Location:United States : Residence Based : Residence Based Division:Enabling Functions Reference Code:866877 Contact Us Email:hrop_usa@bayer.com

• Design, build, and operate a Databricks medallion lakehouse architecture (Bronze/Silver/Gold layers) using Delta Live Tables to support ingestion, transformation, and serving of clinical, behavioral, and operational data across a multi-country digital health platform • Architect and maintain scalable data pipelines on AWS (S3, Glue, Lambda, Kinesis, MSK/Kafka) that ingest data from diverse sources including FHIR-based clinical systems, remote patient monitoring devices, mobile applications, and third-party vendor APIs — ensuring reliability, idempotency, and observability at scale • Implement multi-country data isolation and governance leveraging Databricks Unity Catalog, enforcing data residency requirements across different countries (e.g., the US, EU, and the Kingdom of Saudi Arabia) and integrating policy-as-code consent enforcement (e.g., Open Policy Agent) aligned with regulatory requirements and guidelines (e.g., HIPAA, GDPR) • Partner with platform, compliance, and analytics teams to define and enforce data quality standards, lineage tracking, schema evolution strategies, and tamper-evident audit logging across all tiers of the lakehouse • Support clinical data interoperability by implementing and maintaining FHIR-to-OMOP mapping pipelines, enabling downstream analytics, population health reporting, and AI/ML feature engineering on harmonized datasets • Optimize data platform performance, cost, and reliability through partitioning strategies, compaction, caching, cluster sizing, and monitoring — targeting SLAs appropriate for a patient-facing healthcare platform operating at scale (e.g. 1M+ patients across a dozen markets) • Contribute to certification and compliance readiness (e.g., ISO 27001, SOC 2 Type 2) by maintaining documentation, change control processes, and validation artifacts for all data infrastructure components • Collaborate on real-time and event-driven architectures integrating Kafka-based streaming with the medallion layers and workflow orchestration, supporting adaptive patient journey logic and near-real-time analytics