Data Engineer (Elasticsearch + Datawarehousing)

Accountabilities:: Design, implement, and optimize Elasticsearch clusters to support high-performance querying, indexing, and data retrieval. Build and manage efficient Elasticsearch indexes, ensuring data is structured and stored appropriately for performance and scalability. Design and optimize data storage solutions, including data warehouses, data lakes, and lakehouse environments. Integrate structured and unstructured data from multiple internal and external sources to create unified, analysis-ready datasets. Develop data pipelines and transformation processes that maintain data accuracy, consistency, completeness, and reliability. Gather requirements with product managers and stakeholders and translate business needs into effective technical data solutions. Provide technical recommendations during requirements analysis and contribute to solution design. Participate in Agile ceremonies including sprint planning, stand-ups, reviews, and other collaborative development activities. Develop backend components, APIs, microservices, and automation scripts using Python, Java, and relevant frameworks. Conduct unit and integration testing to validate functionality, reliability, security, and performance. Diagnose and resolve defects, code quality issues, performance bottlenecks, and data-related problems. Maintain clear technical documentation covering data processes, tools, systems, and development practices. Identify opportunities to optimize existing code, improve scalability, strengthen security, and enhance maintainability. Stay current with emerging cloud, data engineering, and distributed processing technologies and incorporate relevant improvements into solutions. Collaborate effectively with engineers, testers, product managers, and other cross-functional stakeholders throughout project delivery. Requirements Bachelor’s degree in Computer Science, Engineering, or a related technical discipline. At least 3 years of professional experience in data engineering or a closely related role. Strong hands-on proficiency with Elasticsearch and Python. Experience working with relational databases such as MySQL or PostgreSQL and NoSQL technologies such as MongoDB. Strong understanding of data warehousing and data lakehouse principles, database architecture, ORM concepts, and data processing. Experience with technologies and frameworks such as Flask, Databricks, Pandas, Spark, PySpark, or similar data engineering tools. Familiarity with machine learning and data analysis libraries such as Scikit-learn or OpenCV is advantageous. Experience using Java to develop or enhance backend systems, particularly where integration with Elasticsearch and databases is involved. Ability to develop APIs, microservices, and automation scripts for data and backend workflows. Familiarity with tools and libraries including logging, requests, subprocess, regex, and pytest. Experience with the ELK stack, Redis, and distributed task queues is a plus. Strong understanding of concurrent and parallel processing concepts. Familiarity with at least one major cloud data engineering ecosystem, such as AWS, Azure, or GCP, with the ability to adapt quickly to different ETL/ELT tools. Experience with Git and collaborative version-control workflows. Comfortable working with Linux environments and creating shell scripts. Solid understanding of software engineering principles, design patterns, testing practices, and maintainable development. Strong analytical and problem-solving abilities with excellent attention to detail. Effective written and verbal communication skills and the ability to collaborate across multidisciplinary teams. Adaptability, curiosity, and willingness to learn new technologies as project requirements evolve. Benefits Fully remote position available across India. Opportunity to work on diverse, impactful data engineering projects and complex client data challenges. Hands-on exposure to Elasticsearch, data warehousing, data lakes, cloud platforms, distributed processing, and modern data technologies. Opportunity to work across both structured and unstructured data environments. Collaborative Agile working environment with cross-functional engineering and product teams. Opportunities for continuous technical learning and exposure to emerging tools and technologies. Scope to contribute to scalable, secure, and high-performance data infrastructure. Supportive team culture focused on empowerment, leadership, innovation, teamwork, and technical excellence. Inclusive workplace with equal employment opportunities based on skills, qualifications, and experience. Compensation structured according to experience, expertise, and skills.