Data Architect

Data Engineer

12 Months (Hybrid role 3 days a week)

Location: St Louis, MO

Only looking for USC/GC.

***No third-party vendor resumes please

Key Responsibilities

Data Engineering & Development

• Design, develop, test, and deploy high-quality, secure, scalable, and resilient data pipelines using Apache Spark, Java/Scala across Hadoop and cloud-native object storage platforms.

• Build and maintain batch and near real-time data processing frameworks capable of supporting petabyte-scale workloads.

• Develop reusable engineering components and frameworks that accelerate data product delivery while maintaining enterprise standards.

Architecture & Platform Engineering

• Design and implement a "build once, run anywhere" architecture supporting seamless deployment across on-premises and public cloud environments without code changes.

• Implement data lineage, metadata management, data cataloguing, data quality controls, and observability capabilities across the data ecosystem.

• Collaborate with architects and platform teams to establish scalable design patterns and engineering best practices.

Cloud Modernisation

• Contribute to migration initiatives moving legacy ETL and data warehouse workloads from on-premises environments to cloud-native architectures.

• Leverage cloud services such as Amazon S3, EMR, Glue, and related data services to improve scalability, reliability, and operational efficiency.

• Drive adoption of modern lakehouse and distributed compute architectures.

Delivery & Technical Leadership

• Lead end-to-end development activities including requirement analysis, solution design, coding, testing, deployment, and production support.

• Mentor and guide junior engineers through code reviews, technical coaching, and engineering best practices.

• Partner with product owners, analysts, architects, and business stakeholders to deliver high-quality solutions within committed timelines.

Operational Excellence

• Troubleshoot complex production incidents and perform root cause analysis to identify and implement long-term remediation strategies.

• Ensure compliance with Mastercard's engineering, security, quality assurance, and operational governance standards.

• Continuously identify opportunities to improve performance, automation, monitoring, and process efficiency.

Innovation

• Evaluate emerging data technologies and conduct proof-of-concept (POC) initiatives to determine their applicability within Mastercard's data ecosystem.

• Contribute to engineering innovation and continuous improvement initiatives across the organisation.

Qualifications

Required Experience

• 7+ years of experience delivering enterprise-scale Data Warehouse, Data Lake, or Data Lakehouse solutions.

• Proven experience implementing multiple end-to-end data engineering projects within large-scale distributed computing environments.

• Hands-on experience migrating ETL and analytics workloads from on-premises platforms to cloud-native environments.

Technical Expertise

• Strong development experience using:Apache Spark, Scala or Java, Hadoop ecosystem technologies, Object Storage platforms

• Experience building orchestration and workflow solutions using: Apache Airflow/ Apache NiFi and Similar enterprise scheduling frameworks

• Strong SQL expertise and experience with Relational and NoSQL database technologies: Oracle/ SQL Server, Cassandra, Dynamo DB etc.

• Working knowledge of cloud platforms, preferably AWS: Amazon S3, EMR, AWS Glue, Cloud-native data services

Professional Skills

• Strong analytical and problem-solving capabilities.

• Experience operating within Agile delivery environments.

• Excellent written and verbal communication skills.

• Proven ability to collaborate within geographically distributed and matrix-based teams.

• Self-starter with strong ownership, accountability, and execution focus.

• Ability to learn emerging technologies quickly and apply them effectively to business challenges.