SENIOR DATA ENGINEER - FULL STACK

Accountabilities: • 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. • Rapidly prototype solutions, validate concepts with stakeholders, and transition successful prototypes into scalable production capabilities. • 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. • Optimize pipelines, queries, streaming workloads, APIs, and applications for performance, reliability, scalability, and cost efficiency. • Troubleshoot and resolve issues across source systems, streaming platforms, pipelines, data models, APIs, applications, and downstream consumers. • Collaborate with platform and product engineering teams to turn recurring stakeholder requirements into reusable data capabilities. • Lead technical design and code reviews, mentor engineers, and contribute to stronger full-stack data-engineering practices and standards. 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. • Strong communication, analytical, problem-solving, technical leadership, and end-to-end ownership skills. • Ability to balance rapid delivery with maintainability, security, governance, scalability, and operational reliability. • Experience with React or another modern frontend framework is a plus. • Familiarity with infrastructure as code, containerization, and automated cloud deployment is advantageous. • Experience with AI/ML pipelines, feature engineering, retrieval systems, or generative AI use cases is beneficial. • Knowledge of data observability, platform engineering, data-product management, or reusable data-platform capabilities is valued. • Background in forward-deployed engineering, solutions engineering, technical consulting, or stakeholder-embedded delivery is helpful. • Prior experience mentoring engineers and working in Agile or Scrum environments is a plus. Benefits • Remote work within India. • Generous time-off policies. • Comprehensive benefits designed to support employees' wellbeing and professional needs. • Education and learning support. • Wellness and lifestyle resources. • Opportunity to work with modern data and cloud technologies, including Databricks, Spark, Kafka, and cloud-native platforms. • Exposure to full-stack data-product development spanning ingestion, modeling, APIs, applications, governance, and production operations. • Opportunities for technical leadership, mentoring, innovation, and professional growth. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1