Staff Data Engineer

Key Responsibilities

• Demonstrate strong expertise in Data Engineering and Data Analytics, owning end-to-end 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, to support long-term maintainability.
• 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.

What you’ll need

Key Responsibilities

• 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.

Required Qualifications

• 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 Skills

• 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 ]
• In addition to our open door policy and supportive work environment, we also strive to provide a competitive salary and benefits package. TJX considers all applicants for employment without regard to race, color, religion, gender, sexual orientation, national origin, age, disability, gender identity and expression, marital or military status, or based on any individual's status in any group or class protected by applicable federal, state, or local law. TJX also provides reasonable accommodations to qualified individuals with disabilities in accordance with the Americans with Disabilities Act and applicable state and local law.