Senior Data Engineer

Mastercard Mastercard · Fintech · O Fallon, MO +1 · AI & Data

Senior Data Engineer for the AI Ops team, responsible for designing, building, and operating analytics and data engineering solutions that provide operational visibility, insight, and proactive intelligence across enterprise infrastructure, applications, and services. This role focuses on integrating data from diverse operational sources, enabling high-quality dashboards and metrics, and supporting AI-driven operational use cases that improve reliability, performance, and decision-making.

What you'd actually do

  1. Design, build, and maintain scalable ETL and data pipelines using tools such as Alteryx, Apache NiFi, Apache Airflow, or similar technologies.
  2. Integrate and normalize data from multiple operational sources, including monitoring platforms, incident and ticketing systems, SLA/SLO reports, surveys, and enterprise data stores.
  3. Develop and maintain analytics and visualizations using tools such as Power BI, Grafana, and related platforms to deliver operational, management, and executive-level insights.
  4. Ensure data quality, consistency, and reliability through validation, monitoring, and continuous improvement of data pipelines.
  5. Translate operational and business questions into measurable indicators, metrics, and dashboards that clearly communicate current state, risks, trends, and improvement opportunities.

Skills

Required

  • Strong experience in data engineering and analytics, working with structured, semi-structured, and unstructured data.
  • Hands-on experience with ETL and workflow orchestration tools such as Alteryx, NiFi, Airflow, or equivalent.
  • Proven ability to build dashboards and visualizations using Power BI, Grafana, or similar tools.
  • Solid understanding of data quality, monitoring, and operational resilience.
  • Strong analytical, problem-solving, and communication skills, with the ability to translate complex data into actionable insights.
  • Experience collaborating across technical and business teams in an operational or enterprise environment.

Nice to have

  • Analytical, investigative and problem-solving skills
  • Strategic thinker with ability to derive and translate data analytics to meet business goals
  • Sound written and verbal communication skills
  • Project management skills, highly organized with strong attention to detail

What the JD emphasized

  • Must be able to work independently in developing and mapping out solutions
  • Must be able to work in a fast paced and dynamic environment, handle multiple tasks, consistently meet established deadlines, and deliver exceptional results

Other signals

  • AI Ops team uses AI, machine learning, and data science techniques to detect anomalies, predict impact, and initiate remediation.
  • Support incident analysis, root cause investigations, and operational reviews through data-driven insights.
  • Partner with AI Ops, SRE, infrastructure, and software engineering teams to operationalize analytics and enable advanced automation and AI/ML use cases.