Staff Data Engineer

Serv, a global executive recruitment partner, is hiring on behalf of our client Mercator.ai for a Staff Data Engineer (Data Pipelines).MercatorAI is building scalable data infrastructure to power high-quality, data-driven decision making at scale. As an early-stage company, the team is focused on creating robust, future-ready systems that can handle complex data ingestion, transformation, and delivery across a growing national footprint.This role is responsible for owning and evolving the company’s data pipeline architecture, ensuring systems are scalable, reliable, and built to support long-term growth. Position OverviewThe Staff Data Engineer is the technical owner of MercatorAI’s data infrastructure and pipeline systems. This individual will lead the design, development, and scaling of distributed data pipelines while remaining hands-on in implementation.This is a builder role suited for someone who enjoys working directly in the code while also setting architectural direction. You will be responsible for scaling existing systems, improving performance and reliability, and integrating modern tools including AI-assisted workflows to enhance engineering output.This role requires strong technical depth, ownership, and the ability to operate effectively in a fast-paced, early-stage environment. Position ResponsibilitiesLead the architecture and evolution of scalable, distributed data pipelines, ensuring high availability and performance at scaleDesign and implement robust data models to support reporting and advanced data applicationsBuild and maintain distributed web scraping systems using tools such as Playwright, Selenium, and BeautifulSoupDevelop systems capable of handling anti-scraping measures, proxy rotation, and high-volume data extractionIntegrate AI and LLMs into engineering workflows for code generation, automation, and optimizationApply prompt engineering techniques to improve data processing, documentation, and troubleshootingIdentify and implement system and process improvements to optimize performance and efficiencyManage and scale cloud-based data infrastructure, including data warehouses, object storage, and search systemsDeploy and maintain containerized workloads using KubernetesImplement data quality monitoring and governance processes to ensure accuracy and reliabilityMentor junior engineers through code reviews, documentation, and knowledge sharingCommunicate technical concepts clearly and provide business context for engineering decisions QualificationsRequired Experience & Skills5+ years of experience in Data Engineering with a track record of scaling systemsExpert proficiency in Python and advanced SQL, including performance tuning and optimizationStrong experience with workflow orchestration tools such as Airflow or Prefect and transformation tools such as dbtProven experience building resilient web scraping systems using Playwright, Selenium, and BeautifulSoupDeep understanding of relational and NoSQL databases including Postgres, MongoDB, and ElasticSearchExperience working with large-scale data systems such as BigQueryStrong proficiency with CI/CD pipelines, Git, and DockerExperience designing and maintaining distributed systems with high availability and fault toleranceNice-to-Have ExperienceExperience with GCP or AWS and Kubernetes for infrastructure managementFamiliarity with LLMs such as ChatGPT, Claude, or Gemini for engineering workflowsExperience with prompt optimization and AI-assisted development Dream TeammateOur ideal teammate:Is highly proactive and takes full ownership of systems and outcomesBalances hands-on execution with long-term architectural thinkingCommunicates complex ideas clearly and effectivelyThrives in fast-paced, early-stage environmentsIs detail-oriented and committed to building high-quality, scalable systemsActively supports and elevates teammates through knowledge sharing and mentorshipBrings curiosity and continuously looks for ways to improve systems and processes Compensation: $180-$200K base Location: Remote (Eastern timezone preferred)