4 days ago

Lead Service Management Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Build and lead end-to-end data engineering for AI and generative AI initiatives within the Architecture & Analytics team. Own data architecture and modeling, design scalable ETL/ELT pipelines with low-latency reliability, and ensure data quality, governance, and anonymization for sensitive data. Provide hands-on technical leadership through code reviews and best practices while collaborating with engineers, scientists, and product stakeholders to deliver data components in agile sprints.

Location: United States

Workplace: Onsite

Employment Type: Full time

Job Function: Administration & Executive Assistance

Seniority: Mid level

Key Responsibilities

•Own data architecture and modeling strategy for AI projects, defining how data is stored, organized, and accessed.
•Lead development of robust ETL/ELT workflows and data models, delivering large datasets with high reliability and low latency for training and inference.
•Oversee data governance and compliance, including data anonymization, quality checks, lineage, and controls for sensitive information.
•Provide hands-on technical leadership across data engineering projects, including code reviews and enforcing best practices.
•Collaborate with engineers, scientists, and product stakeholders to scope work and deliver data components aligned with agile sprint milestones.

Pay and Benefits

Salary: USD 122,000 - 207,000 annually

Perks:Health InsuranceDentalVision401kPaid Leave

Key Requirements

•8–12+ years in data engineering or backend engineering, including senior/lead roles, designing end-to-end data systems and production pipelines.
•Strong Python and/or Java/Scala skills with deep experience in Spark, Hadoop, Hive/Impala, and Airflow.
•Hands-on cloud experience with AWS, Azure, or GCP, including services such as S3, Glue, EMR, or Data Factory.
•Knowledge of AI/ML data lifecycle including dataset preparation and feature/label management, with experience supporting real-time or batch training pipelines.
•Ability to translate ambiguous goals into clear plans and provide leadership, mentorship, and rigorous code review.

Experience:8-12 years

Skills:LeadershipCode reviewsProblem-solvingMentorshipCross-functional collaborationData governanceSystems thinking

Tech Stack:PythonJavaScalaSparkHadoopHiveImpalaAirflowAWSAzureGCPS3GlueEMRData FactoryETLELTAIMachine learningGenerative AI

Company Brief

Mastercard

Mastercard is a global payments and technology company that processes transactions, provides fraud prevention and data services, and builds payment infrastructure for consumers, businesses, merchants and governments across 200+ countries.

Industry: Payments

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Purchase, New York, United States

Founded: 1966

Glassdoor: 4.1

Website LinkedIn Glassdoor

Loading job details...

Preparing the role view and application actions.

Mastercard

4 days ago

Lead Service Management Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Location: United States

Workplace: Onsite

Employment Type: Full time

Job Function: Administration & Executive Assistance

Seniority: Mid level

Key Responsibilities

•Own data architecture and modeling strategy for AI projects, defining how data is stored, organized, and accessed.
•Lead development of robust ETL/ELT workflows and data models, delivering large datasets with high reliability and low latency for training and inference.
•Oversee data governance and compliance, including data anonymization, quality checks, lineage, and controls for sensitive information.
•Provide hands-on technical leadership across data engineering projects, including code reviews and enforcing best practices.
•Collaborate with engineers, scientists, and product stakeholders to scope work and deliver data components aligned with agile sprint milestones.

Pay and Benefits

Salary: USD 122,000 - 207,000 annually

Perks:Health InsuranceDentalVision401kPaid Leave

Key Requirements

•8–12+ years in data engineering or backend engineering, including senior/lead roles, designing end-to-end data systems and production pipelines.
•Strong Python and/or Java/Scala skills with deep experience in Spark, Hadoop, Hive/Impala, and Airflow.
•Hands-on cloud experience with AWS, Azure, or GCP, including services such as S3, Glue, EMR, or Data Factory.
•Knowledge of AI/ML data lifecycle including dataset preparation and feature/label management, with experience supporting real-time or batch training pipelines.
•Ability to translate ambiguous goals into clear plans and provide leadership, mentorship, and rigorous code review.

Experience:8-12 years

Skills:LeadershipCode reviewsProblem-solvingMentorshipCross-functional collaborationData governanceSystems thinking

Tech Stack:PythonJavaScalaSparkHadoopHiveImpalaAirflowAWSAzureGCPS3GlueEMRData FactoryETLELTAIMachine learningGenerative AI

Company Brief

Mastercard

Industry: Payments

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Purchase, New York, United States

Founded: 1966

Glassdoor: 4.1

Website LinkedIn Glassdoor