Data Engineer

Lead Data Engineer

Experience- 10 years

Location- Bangalore

Overview

We are seeking a highly experienced Lead Data Engineer to lead the design, development, and delivery of scalable, reliable, and cost-efficient data platforms. The ideal candidate will possess deep expertise in modern data engineering technologies, including distributed data processing, ETL/ELT pipeline development, data modeling, and workflow orchestration, along with hands-on experience with the Databricks Lakehouse Platform and its medallion (Bronze/Silver/Gold) architecture

.This role also requires a solid understanding of Large Language Models (LLMs) and GenAI data ecosystems, enabling the development of high-quality datasets and retrieval-ready pipelines for AI-powered applications. As a technical leader, you will mentor engineering teams, establish best practices, and collaborate closely with architects, data scientists, and business stakeholders to deliver robust data solutions

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Key Responsibilities

• Lead the architecture, development, and optimization of scalable data platforms supporting both batch and streaming workloads.
• Design, implement, and maintain ETL/ELT pipelines for data ingestion, transformation, and curation using the Databricks Lakehouse Platform and medallion architecture.
• Define data models, schemas, and storage strategies for data lakes and data warehouses to support analytics, reporting, machine learning, and GenAI initiatives.
• Develop and curate high-quality datasets, feature engineering pipelines, and retrieval-ready data stores, including embeddings and vector-based data structures, for LLM-powered applications.
• Establish engineering standards, coding best practices, code review processes, and CI/CD pipelines to ensure maintainable and reliable solutions.
• Build and automate end-to-end data workflows using orchestration tools such as Apache Airflow or equivalent platforms.
• Lead migrations from legacy on-premises or cloud-based data warehouses to modern cloud-native and lakehouse architectures.
• Optimize performance and cost by implementing effective partitioning, caching, compute tuning, and distributed processing strategies.
• Implement robust data governance, security, lineage, and access control frameworks aligned with organizational compliance requirements.
• Build monitoring, logging, alerting, and data quality frameworks to ensure reliability and proactive issue resolution.
• Mentor and guide data engineers while collaborating with architects, data scientists, and business stakeholders to translate business requirements into scalable technical solutions.
• Participate in and lead Agile ceremonies, including sprint planning, stand-ups, retrospectives, and technical reviews.

Required Qualifications

• 10+ years of hands-on experience in data engineering, including leadership of enterprise-scale data platform initiatives.
• Strong expertise with the Databricks Lakehouse Platform, including Delta Lake, Delta Live Tables, Databricks Workflows, and Unity Catalog.
• Proven experience implementing the medallion (Bronze/Silver/Gold) architecture for enterprise data platforms.
• Deep knowledge of distributed data processing using Apache Spark, including PySpark and Spark SQL.
• Extensive experience building scalable ETL/ELT pipelines for both batch and streaming data processing.
• Expert proficiency in Python and SQL for data engineering, transformation, validation, and pipeline development.
• Strong experience designing and managing data lakes and data warehouses using dimensional and lakehouse modeling techniques.
• Practical understanding of Large Language Models (LLMs) and GenAI concepts, including prompts, embeddings, vector databases, Retrieval-Augmented Generation (RAG), and supporting data pipelines.
• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and its core data services.
• Demonstrated success leading migrations from legacy platforms such as Hadoop or traditional data warehouses to modern cloud and lakehouse environments.
• Strong expertise in distributed computing, data partitioning, and performance optimization techniques.
• Experience implementing data security, governance, lineage, encryption, IAM, and metadata management.
• Solid understanding of object-oriented programming principles, software design patterns, and CI/CD practices.
• Experience working within Agile/Scrum environments and mentoring engineering teams.
• Excellent analytical, problem-solving, stakeholder management, and communication skills.

Preferred Qualifications

• Experience developing LLM-powered applications or AI data pipelines using frameworks such as LangChain, Llama Index, or similar technologies.
• Hands-on experience with vector databases, including Pinecone, Weaviate, FAISS, or pgvector.
• Industry certifications in Databricks, AWS, Azure, or Google Cloud Platform.
• Experience with streaming technologies such as Spark Structured Streaming or Apache Kafka.
• Familiarity with Infrastructure as Code (Terraform) and DevOps tools such as Git, Jenkins, or Azure DevOps.
• Exposure to MLOps and LLMOps practices, including model deployment and lifecycle management.
• Experience working with business intelligence and visualization tools such as Power BI, Tableau, or Amazon QuickSight.