Architect, implement, and optimize ETL/ELT workflows using tools such as Databricks and Informatica to ingest and process large datasets. Deploy and manage data infrastructure on Azure and AWS, including containerized services and serverless components. Partner with data scientists, software engineers, and business stakeholders to translate requirements into scalable data solutions. Monitor and tune pipeline performance, implement robust monitoring/alerting (e.g., Prometheus, DataDog), and troubleshoot production issues. Define and enforce data validation rules, lineage tracking, and governance standards to ensure accurate and compliant data usage. Build CI/CD pipelines for data code, automate deployments, and support model operationalization workflows. Share knowledge of data engineering best practices, conduct code reviews, and mentor junior engineers. ​

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field. ​ 8+ years of handson experience designing and building data pipelines and infrastructure. ​ Strong proficiency in data engineering tools and technologies, such as Apache Spark, Hadoop, Kafka, and cloud-based platforms (AWS, Azure, or GCP). Hands-on experience with data modeling, ETL processes, and distributed data processing frameworks. Proficiency in programming languages like Python, R, Java, and expertise in SQL. Solid understanding of data architecture principles, microservices, and data governance frameworks. Excellent problem-solving skills and ability to drive solutions in a fast-paced environment. Strong communication and collaboration skills, with a proven ability to work with both technical and non-technical stakeholders.