Principal Data Engineer

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Job Description

Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals

Define data requirements, gather and mine large-scale structured and unstructured data, and validate data using various data tools in the Big Data environment

Design and lead the development of AI agents, skills, and MCPs (Model Context Protocols), including defining and enforcing guardrails to ensure responsible, secure, and scalable AI-driven solutions

Support Data Scientists in data sourcing and preparation to visualize data and synthesize insights of commercial value

Lead the evaluation, implementation, and deployment of emerging tools and processes for analytic data engineering to improve productivity

Develop and deliver communication and education plans on analytic data engineering capabilities, standards, and processes

Partner with business analysts and solutions architects to develop technical architectures for strategic enterprise projects and initiatives

Mentor and coach other data and analytic professionals on data standards and practices

Design and evolve Data Platform architecture and its core components, ensuring scalability, resilience, and alignment with enterprise data strategy

Provide cross-team (cross‑pod) technical guidance, particularly on end-to-end data flow design, promoting adoption of approved patterns, standards, and best practices

Act as a proactive technical SME, unblocking delivery teams through structured support mechanisms and hands-on problem resolution

Define and align the team’s strategic roadmap with organizational objectives, ensuring key initiatives such as framework scaling, technology assessments, and “Data as a Product” are prioritized for maximum business impact

Design robust data and solution architectures leveraging technologies such as Delta Live Tables (DLT), API-based ingestion, Databricks OLTP patterns, Kafka, and other modern platform components

Collaborate closely with leadership to establish scalable engineering standards, including CI/CD standardization, repository structure, reusable framework components, and governance mechanisms that reduce delivery time and operational incidents across teams

Skills and qualifications:

Expert in designing and maintaining scalable data architectures, capable of handling complex data systems

Proficient in SQL and other data manipulation languages, with a strong ability to optimize and troubleshoot queries

Skilled in DevSecOps practices and continuous integration to enhance workflow efficiencies

Experienced in software development methodologies, contributing to both project and operational goals

Knowledgeable in data structures and algorithms, enhancing data processing and storage solutions

Familiar with distributed data technologies like Spark and Hadoop, capable of managing large-scale data operations

Efficient in system integration, ensuring seamless data flow and connectivity across platforms

Understanding Agentic AI skillsets and applying capabilities such as planning, decision-making, tool integration, and autonomous task execution to build robust and high-performance AI solutions.

Proficient in programming languages such as Python, Scala, Java, and C++, applying them effectively in data engineering tasks

Experienced in SQL database management, performance tuning, and complex query handling

Skilled in cloud platform deployment and tools like Kubernetes, optimizing cloud-based data solutions

Capable of performing advanced troubleshooting and guiding others through complex problem-solving processes

Locations

IND - Bengaluru

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time