Amtex Systems Inc is an information technology and talent solutions company offering talent and BI consulting to the companies in US for over 25 years.
Our solutions are designed to fill resource gaps, by providing the right candidates who deliver value to the organization. Our propensity to nurture and build strong relationships with our clients helps us better understand their business demands and gives us the ability to provide services that are on time and rise above the rest.
Sr. Data Engineer
Philadelphia, PA - 2 days onsite
Fulltime
Must Haves: 5+ years of exp. | Big Data | Cloud/On Prem | DevOps | CI/CD |Data Services/APIs/Microservices | DataBricks |(PySpark, Spark SQL, Delta Lake) | Spark | Azure Cloud Services | Azure Event Hubs | Azure Stream Analytics | ETL/ELT.
Role Description
As a Senior Data Engineer, you will play a key role in driving the design and architecture of data pipelines that consume real-time streaming data from connected medical devices. You will collaborate cross-functionally with business analysts, software engineers, machine learning engineers, and business users to implement technical approaches and infrastructure that support data consumption across the organization.
This is a great opportunity for someone who is passionate about data engineering, thrives in environments where you can take ownership and recommend solutions, and is a self-starter who is open to learning new tools as needed.
Qualifications
Education
• Bachelor's degree in Computer Science, Mathematics, Engineering, or a related technical field and 6+ years of professional experience in data engineering, analytics, or warehousing
• OR Master's degree in a related technical field and 3+ years of professional experience in data engineering, analytics, or warehousing
Required Experience
• 5+ years of experience designing, building, and operating big data and real-time streaming pipelines across cloud and on-premises environments
• 5+ years applying DevOps and CI/CD practices to data and analytics workloads
• Production experience building data services, APIs, or microservices for downstream data consumption
Required Technical Skills
• Strong, hands-on experience designing and building production-grade data pipelines on Databricks
• Demonstrated experience with Spark optimization and tuning in Databricks, including performance analysis, partitioning strategies, caching, shuffle optimization, and cost-aware pipeline design
• Strong experience with Azure cloud services for data engineering and streaming workloads
• Experience building data services, APIs, or microservices for data consumption.
• Data quality, validation, and privacy-aware handling for regulated or sensitive data
Preferred Technical Skills
• Azure Event Hubs and Azure Stream Analytics for IoT and telemetry ingestion and stream processing
• Additional Azure data and integration services supporting batch and streaming architectures
• Collaboration with ML teams: feature pipelines, training data, or streaming data for model development
Key Responsibilities
• Design and build batch and streaming data pipelines on Azure and Databricks, with primary focus on real-time IoT and telemetry use cases within a Medallion architecture.
• Develop ETL/ELT workflows to ingest, transform, and validate large volumes of structured and unstructured data.
• Build and maintain data services, APIs, and microservices for application, analytics, and ML/AI teams.
• Implement real-time streaming solutions using Azure Event Hubs, Azure Stream Analytics, and related Azure integration patterns, with cost-effective throughput, partitioning, and downstream delivery to Databricks.
• Optimize production Databricks pipelines using PySpark, Spark SQL, and Delta Lake, including Spark tuning for performance, reliability, and cost.
• Troubleshoot and resolve complex pipeline issues across Databricks, Azure, and on-premises systems, including root cause analysis and corrective action.
• Partner with data analysts, software engineers, ML engineers, and business stakeholders to translate requirements into technical designs and delivery priorities.
• Apply data quality, validation, and privacy-first practices, and deliver reliable pipelines through software engineering standards, documentation, testing, and CI/CD.
Outcomes
• Onboard to the Azure Databricks environment and contribute to troubleshooting, stabilization, and optimization of existing batch and streaming data pipelines.
• Stand up Azure streaming ingestion for telemetry data and deliver production-ready pipelines integrated with Databricks to support API, ML, and downstream analytics consumption within the Medallion architecture.
• Design and deliver data services or consumption patterns that enable business and ML teams to access near-real-time telemetry data reliably, securely, and on a scale.
Regards,
Puneet.
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