Senior Data Platform Engineer

Senior Data Platform EngineerHandraise is hiring a Senior Data Platform Engineer to help design, build, and scale the data infrastructure powering our AI-native media and narrative intelligence platform. This is not a traditional backend engineering role. You will work on the systems that ingest, process, enrich, organize, and serve massive volumes of global news and digital content so that our customers can understand the narratives shaping their brands, industries, competitors, and reputations. You will be responsible for building reliable data pipelines, improving the performance and quality of our data platform, and helping turn unstructured content into structured intelligence that our AI systems can reason over. Your work will directly support critical product capabilities including narrative clustering, brand-centric sentiment, publication intelligence, competitive analysis, LLM perception, citation intelligence, and executive-level briefings. You will work closely with engineering, product, data science, and company leadership to make foundational technical decisions that influence the future of the platform. You will also help establish the architecture, standards, and operating practices required to scale a rapidly growing enterprise product. If you are someone who enjoys solving hard data problems, can operate without a perfect playbook, and wants to build infrastructure that sits at the center of a differentiated AI product, this role is for you. Your ImpactDesign, build, and maintain scalable pipelines for ingesting and processing large volumes of global news, media, and digital content Improve the reliability, throughput, latency, observability, and cost efficiency of the Handraise data platform Develop systems that transform unstructured content into structured, queryable, and AI-ready data Build and maintain data models, enrichment workflows, APIs, and services used across the Handraise platform Support core intelligence capabilities including narrative clustering, brand-centric sentiment, publication tiering, entity resolution, prominence, social engagement, and LLM perception analysis Partner with product and data science teams to productionize new models, signals, and enrichment capabilities Build systems that allow our AI applications to retrieve the correct context and generate trustworthy, traceable answers Improve data quality by identifying ingestion failures, duplication, coverage gaps, schema inconsistencies, and enrichment errors Establish monitoring, alerting, testing, and recovery processes across critical data workflows Help make architectural decisions related to storage, indexing, orchestration, streaming, batch processing, and data serving Contribute to security, privacy, governance, auditability, and enterprise-readiness across the platform Help define the engineering standards and technical foundation for a rapidly growing data organization Mentor other engineers and raise the technical bar across the team Your Skills6+ years of professional software engineering experience, with significant experience building data-intensive systems Strong experience designing and operating production data pipelines at scale Advanced proficiency in Python and SQL Experience working with distributed systems, cloud infrastructure, data warehouses, object storage, and large-scale search or indexing technologies Experience with batch and streaming data architectures Strong understanding of data modeling, schema design, partitioning, orchestration, and pipeline reliability Experience building systems that process large volumes of structured and unstructured data Experience with observability, monitoring, automated testing, incident response, and production troubleshooting You understand how to balance speed, scalability, reliability, and cost You can move between architecture-level thinking and hands-on implementation You are comfortable diagnosing difficult problems across pipelines, infrastructure, application code, and third-party data sources You can communicate technical tradeoffs clearly to both technical and non-technical stakeholders You are comfortable operating in ambiguity. Unrefined processes, evolving requirements, and incomplete information do not slow you down You are proactive and take ownership. You do not wait for direction when you see an important problem You care deeply about data accuracy, system reliability, and the downstream consequences of poor-quality data Non-NegotiablesYou understand that this is an early-stage startup and you will be wearing multiple hats You are excited by the opportunity to build an innovative company and technical platform from the ground up You do not have a "that is not my job" attitude. If it needs to be done, we all have to step up and get it done You are willing to work directly in production systems, investigate data issues, and help resolve urgent customer-impacting problems You are comfortable making pragmatic decisions with imperfect information You care about building systems that are not only technically elegant, but commercially valuable You are curious and passionate about the use and impact of AI You are not afraid of learning complex media, communications, search, machine learning, and LLM concepts You take responsibility for the quality, reliability, and performance of the systems you build Nice to HavesExperience building media monitoring, search, intelligence, analytics, or social listening platforms Experience with OpenSearch, Elasticsearch, or similar large-scale search infrastructure Experience with modern data orchestration and transformation tools Experience building event-driven or streaming architectures Experience working with NLP, machine learning, LLM, retrieval, or AI-enrichment pipelines Experience with entity resolution, document classification, clustering, semantic search, embeddings, or knowledge graphs Experience integrating and normalizing data from multiple third-party providers Experience supporting enterprise-grade applications with strict uptime and data-quality expectations Experience with infrastructure as code, containerization, and cloud-native deployment practices Early-to-mid-stage startup experience Experience mentoring engineers or serving as a technical lead Our BenefitsCompetitive compensation with equity Very competitive health benefits, including medical, dental, vision, and life insurance Opportunity to shape the architecture and technical foundation of an AI-native platform from the ground floor Direct influence over product strategy and company direction High ownership, high autonomy, and the opportunity to solve difficult, meaningful technical problems The chance to build proprietary data and AI infrastructure used by some of the world's largest and most influential brands A highly collaborative team that values speed, accountability, curiosity, and exceptional work Sound Great? What Next?Apply below. After reviewing resumes, selected candidates will be invited to an initial phone screening, followed by technical and in-person interviews with members of the Handraise engineering and leadership teams. Handraise is dedicated to equal employment opportunities. We prioritize creating a diverse and inclusive workplace, free of harassment and discrimination. We believe that hiring talented individuals from diverse backgrounds is not only just but also vital to our strength as a company. Our customer base is diverse, and so is our team. Therefore, we welcome job candidates from all backgrounds. Everyone will receive consideration for employment without regard to any status protected by federal, state, or local laws.