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.