Data Engineering offshore

As a Senior Data Engineer, you will: • Design, develop, and support scalable batch and near real-time data pipelines, data products, and lakehouse-ready data assets using Spark, Python, SQL, Hive, Databricks, and cloud-native technologies. • Build and optimize capabilities supporting the ONE Data Lake and Mastercard's unified data platform strategy. • Develop ingestion, transformation, and orchestration solutions for batch and streaming/near real-time data patterns using technologies such as Cribl, SSIS, Apache Ozone, Cloudera Data Services, Hive, Spark, Azure, Microsoft Fabric, Databricks, and related ecosystem tools. • Enable trusted, governed, and accessible enterprise data through the implementation of data quality, lineage, and governance standards. • Partner closely with Product, TPM, Data Governance, Architect, and Engineering teams to deliver strategic business outcomes. • Support AI, analytics, reporting, and decisioning use cases by delivering scalable and reliable data assets. • Drive modernization initiatives across hybrid data environments and cloud. • Improve operational efficiency through automation, monitoring, observability, and self-service capabilities. • Troubleshoot complex platform and data issues, perform root cause analysis, and implement sustainable solutions. • Evaluate emerging technologies and identify opportunities to advance Team ONE's data capabilities. Required Qualifications • 6+ years of experience delivering enterprise-scale data engineering, data warehousing, or big data solutions. • Strong hands-on experience with Apache Spark, Python, SQL and Databricks. • Experience building and maintaining large-scale data pipelines and distributed data processing systems. • Experience with streaming or near real-time ingestion patterns, including event/log data collection, routing, transformation, and delivery into enterprise data lake or lakehouse platforms. • Experience with Hive, Apache Ozone, or similar data integration, Azure, Microsoft Fabric, and orchestration technologies. • Strong understanding of data architecture, ETL/ELT design patterns, and modern data platform principles. • Hands-on experience with Microsoft SQL Server, SSIS packages, stored procedures, SQL performance tuning, and relational database concepts. • Experience implementing data quality, governance, metadata, and lineage capabilities. • Proven ability to analyze complex production issues, perform root cause analysis, and implement long-term solutions. • Strong communication and collaboration skills with the ability to work effectively across technical and business teams. • Demonstrated ability to manage competing priorities in a fast-paced environment. • Strong ownership mindset and commitment to delivering high-quality solutions. Required Qualifications • 6+ years of experience delivering enterprise-scale data engineering, data warehousing, or big data solutions. • Strong hands-on experience with Apache Spark, Python, SQL and Databricks. • Experience building and maintaining large-scale data pipelines and distributed data processing systems. • Experience with streaming or near real-time ingestion patterns, including event/log data collection, routing, transformation, and delivery into enterprise data lake or lakehouse platforms. • Experience with Hive, Apache Ozone, or similar data integration, Azure, Microsoft Fabric, and orchestration technologies. • Strong understanding of data architecture, ETL/ELT design patterns, and modern data platform principles. • Hands-on experience with Microsoft SQL Server, SSIS packages, stored procedures, SQL performance tuning, and relational database concepts. • Experience implementing data quality, governance, metadata, and lineage capabilities. • Proven ability to analyze complex production issues, perform root cause analysis, and implement long-term solutions. • Strong communication and collaboration skills with the ability to work effectively across technical and business teams. • Demonstrated ability to manage competing priorities in a fast-paced environment. • Strong ownership mindset and commitment to delivering high-quality solutions. Preferred Qualifications • Experience with Cribl or similar observability/log streaming platforms, including data routing, filtering, enrichment, and delivery to downstream lakehouse or analytics platforms. • Experience with Microsoft Fabric, OneLake, and cloud-native data platforms. • Experience with enterprise data governance and metadata management solutions. • Experience supporting analytics, reporting, AI, or machine learning workloads. • Experience with Power BI and enterprise reporting environments. • Experience mentoring engineers and leading technical initiatives.