Senior Data Engineer - Full Stack
New
J
JobgetherData Engineering
Remote work within India.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLCloud ComputingKafkaSparkCI/CDRESTful APIsDatabricksPySpark
Requirements
- 5+ years of experience in data engineering, software engineering, or a related discipline, including ownership of production data solutions.
- Strong proficiency in SQL, Python, and PySpark, with experience developing reliable, production-grade data pipelines and products.
- Hands-on experience with Databricks, Apache Spark, Delta Lake, and Unity Catalog, or comparable data-platform and governance technologies.
- Experience building and supporting streaming or near-real-time pipelines using Kafka, Kinesis, Event Hubs, or similar technologies.
- Strong understanding of event-driven architecture, message processing, schema evolution, data consistency, and streaming reliability.
- Experience delivering full-stack solutions spanning data pipelines, backend services or APIs, and lightweight user-facing applications.
- Experience developing REST APIs, services, and integrations, ideally using Python frameworks such as FastAPI, Flask, or comparable tools.
- Experience with AWS, Azure, or GCP and cloud-native architecture patterns.
- Strong knowledge of data modeling, data warehousing, distributed processing, and analytics-oriented data design.
- Experience with Git, automated testing, CI/CD, monitoring, and production deployment practices.
- Demonstrated ability to work directly with stakeholders, navigate ambiguity, and translate business challenges into practical technical solutions.
Responsibilities
- Partner with business and technical stakeholders to understand workflows, clarify objectives, translate ambiguous requirements into technical solutions, and establish delivery plans.
- Design, develop, and maintain end-to-end data products using Databricks, Delta Lake, SQL, Python, PySpark, and related technologies.
- Build reliable batch, incremental, streaming, and near-real-time data pipelines using Kafka and comparable event-streaming technologies.
- Design event-driven architectures and integrate operational systems with downstream data consumers.
- Develop backend services, REST APIs, and integrations that expose governed data to applications and operational workflows.
- Build lightweight applications, dashboards, and user interfaces in collaboration with product, analytics, BI, and UX teams.
- Create scalable data models and curated datasets supporting analytics, reporting, AI/ML initiatives, and operational decision-making.
- Implement data-quality, security, lineage, and governance controls using Databricks, Unity Catalog, and comparable technologies.
- Establish automated testing, CI/CD, monitoring, alerting, documentation, and deployment practices across the complete data-product lifecycle.
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