Senior Data Engineer.

Experience : 6 to 10 years

Qualification : BE/B tech, ME/M tech, Bsc, Msc.

Location : Hyderabad, Bangalore and Mumbai 

As a Senior Data Engineer, you will own the end-to-end data platform, from raw data ingestion through transformation to trusted, business-ready datasets. You will be responsible for designing, building, and maintaining scalable, reliable, and high-quality data pipelines that power analytics and decision-making across the organization.

This role requires someone who can make sound architectural decisions, balance trade-offs, and deliver production-grade solutions in a fast-paced environment. You'll partner closely with analysts, engineers, and business stakeholders, ensuring data solutions are reliable, maintainable, and aligned with business needs. A strong bias for action, systems thinking, and expertise in modern data engineering practices are essential.Key Responsibilities

• Design, build, and maintain end-to-end data pipelines from ingestion to curated, trusted data models.

• Develop scalable and maintainable data transformation layers using modern transformation frameworks.

• Design and optimize data warehouse schemas and data models to support reporting and analytics.

• Write highly efficient, production-grade SQL and optimize complex queries for performance.

• Build, maintain, and monitor orchestration workflows, ensuring proper dependency management and data lineage.

• Implement data quality, validation, monitoring, and reliability practices to ensure trusted datasets.

• Make architecture and design decisions while balancing scalability, maintainability, and delivery speed.

• Collaborate closely with analysts, engineers, and business stakeholders to understand requirements and translate them into robust data solutions.

• Proactively identify improvements in data architecture, pipeline performance, and engineering practices.

• Leverage Large Language Models (LLMs) and AI-assisted development tools to improve engineering productivity and accelerate delivery.

• Build solutions that are production-ready, scalable, and resilient while maintaining high development velocity.

• Expert-level proficiency in SQL, including complex query optimization, performance tuning, and advanced data modeling.

• Strong expertise in data warehousing concepts, dimensional modeling, and analytical database design.

• Hands-on experience with modern data transformation frameworks such as:

  • dbt

  • SQLMesh

• Strong understanding of workflow orchestration, DAG design, dependency management, and data lineage.

• Experience designing and maintaining reliable, production-grade data pipelines.

• Strong knowledge of data quality frameworks, testing, validation, and monitoring.

• Excellent systems thinking with the ability to design scalable and maintainable data architectures.

• Proven ability to deliver high-quality solutions in fast-paced, agile environments.

Demonstrated hands-on experience using Large Language Models (LLMs) as a core part of the software development lifecycle, including:

• Prompt engineering

• Context engineering

• Retrieval-Augmented Generation (RAG)

• Vector embeddings and vector search

• Token optimization and context management

• LLM evaluations and quality assessment

• Guardrails and hallucination mitigation

• Function calling, tool use, and AI workflow orchestration

Candidates should have practical experience integrating AI into engineering workflows rather than limited exposure or experimentation.

• Strong communication and stakeholder management skills.

• Ability to challenge requirements constructively and provide technical guidance.

• Strong ownership mindset with the ability to make informed technical decisions independently.

• Bias toward execution, continuous improvement, and delivering business value.

• Hands-on experience building and operating data platforms on Databricks.

• Experience with AI-assisted or "vibe coding" approaches to software development.

• Exposure to fintech, banking, payments, or financial services data platforms.

• Experience designing scalable data architectures in cloud-native environments.

• Familiarity with modern DevOps, CI/CD, Infrastructure as Code (IaC), and observability practices.

• Experience working in high-growth startups or small engineering teams with broad ownership.