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.

Originally posted on Himalayas