Staff Data Engineer

Overview

Staff Data Engineer on the AI enablement team within Enterprise Data & Analytics, driving responsible adoption and deployment of AI capabilities and solutions at TJX.

What you'll do

• Own end-to-end data engineering responsibilities including data ingestion, transformation, modeling, optimization, and enabling analytics and reporting use cases.
• Leverage Generative AI and Large Language Models (LLMs) to enhance Data Engineering and Data Analytics solutions, driving automation, insight generation, and productivity improvements.
• Develop and consume APIs and services to enable seamless interaction between AI models, data platforms, and downstream applications, supporting efficient model serving and integration.
• Optimize AI and ML solution performance through techniques such as parameter tuning, inference optimization, and resource-efficient deployment strategies.
• Monitor, maintain, and support AI-enabled systems in production, proactively troubleshooting issues to ensure stability, performance, and availability.
• Work closely with cross-functional teams, including data engineers, software engineers, architects, and business stakeholders, to deliver end-to-end data and AI solutions.
• Produce clear and comprehensive technical documentation, including solution designs, implementation guidelines, and operational runbooks.
• Design, code, deploy, and support high-quality software components, partnering with architects and senior engineers to build robust, AI-enabled systems and services.
• Lead medium to large technical initiatives by breaking down work, guiding team members, resolving technical challenges, and ensuring timely and successful delivery.
• Design, develop, and deploy scalable, cloud-native data engineering solutions on Azure using Databricks, ADLS Gen2, and Snowflake.
• Build and operationalize end-to-end data pipelines for batch and streaming workloads, enabling reliable ingestion, transformation, and consumption of structured and unstructured data.
• Leverage Generative AI and LLM capabilities to enhance data engineering workflows, including data ingestion automation, schema inference, data quality validation, metadata enrichment, and analytics enablement.
• Implement and manage modern data architectures such as Lakehouse patterns using Delta Lake, optimizing data reliability, performance, and governance.
• Develop secure, scalable APIs and data services to expose curated datasets and GenAI-enabled insights to downstream analytics and applications.
• Implement CI/CD pipelines for data engineering workloads using Azure DevOps or GitHub, supporting automated testing, deployment, and environment promotion.
• Ensure data platform security and compliance by applying best practices such as encryption, access controls, role-based permissions, and secure data sharing.
• Monitor and troubleshoot data pipelines and platforms using tools such as Databricks monitoring, Azure Monitor, Log Analytics, and related observability solutions.
• Collaborate in an Agile/Scrum environment with data scientists, analytics engineers, architects, and business stakeholders to deliver end-to-end data solutions.
• Create and maintain comprehensive documentation, including data models, pipeline designs, operational runbooks, and GenAI integration guidelines.
• Apply responsible and ethical AI principles when enabling GenAI use cases on data platforms, including data quality, lineage, transparency, and bias awareness.
• Stay current with advancements in data engineering, Databricks, Snowflake, cloud-native data services, and emerging GenAI patterns relevant to analytics and data platforms.

What you'll need

• Bachelor's degree in computer science, Engineering, or a related field.
• 7+ years of hands-on Data Engineering development experience with production deployments.
• Strong proficiency in Python, SQL, and modern software engineering practices.
• Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, and core Azure cloud services.
• Expertise in version control (Git), CI/CD pipelines for ML, and cloud DevOps workflows.
• Practical experience with data engineering tools and scalable data processing (e.g., Databricks, PySpark).
• Strong communication, analytical thinking, and problem-solving abilities.
• Knowledge of security best practices for AI systems and responsible AI considerations.

Nice to have

• Experience with LLM application frameworks such as LangChain, Semantic Kernel, and LlamaIndex.
• Experience with multimodal AI use cases and advanced GenAI workloads.
• Knowledge of feature stores and ML operations tools such as Feast, Tecton, Evidently.
• Azure Cloud Data Engineering (ADF, Databricks, Logic App, ADLS, DevOps etc.).

Details

• Location: Salarpuria Sattva Knowledge City, Inorbit Road, Hyderabad, India (APAC Home Office).