{"id":4028428,"url":"https://alion.io/job/tjx-staff-data-engineer","title":"Staff Data Engineer","company":{"id":1758993,"name":"TJX","domain":"tjx.com","url":"https://alion.io/company/tjx-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"LLM","optional":false},{"name":"OpenAI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scrum","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Feast","optional":true},{"name":"Feature Store","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Multimodal AI","optional":true},{"name":"Semantic Kernel","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-11-10T17:35:30Z","employer_posted_date":null,"last_verified_at":"2026-11-10T17:35:30Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"What you'll discover\n\nWhat you'll do:\nDemonstrate 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.\nLeverage Generative AI and Large Language Models (LLMs) to enhance Data Engineering and Data Analytics solutions, driving automation, insight generation, and productivity improvements.\nDevelop and consume APIs and services to enable seamless interaction between AI models, data platforms, and downstream applications, supporting efficient model serving and integration.\nOptimize AI and ML solution performance through techniques such as parameter tuning, inference optimization, and resource-efficient deployment strategies.\nMonitor, maintain, and support AI-enabled systems in production, proactively troubleshooting issues to ensure stability, performance, and availability.\nWork closely with cross-functional teams, including data engineers, software engineers, architects, and business stakeholders, to deliver end-to-end data and AI solutions.\nProduce clear and comprehensive technical documentation, including solution designs, implementation guidelines, and operational runbooks, to support long-term maintainability.\nDesign, code, deploy, and support high-quality software components, partnering with architects and senior engineers to build robust, AI-enabled systems and services.\nLead medium to large technical initiatives by breaking down work, guiding team members, resolving technical challenges, and ensuring timely and successful delivery.\n\nEnterprise Data & Analytics thrives on strong relationships with our business and IT partners and working diligently to address their needs which support TJX growth and operational stability. On this tightly knit and fast-paced team you will be constantly challenged to stretch and think outside the box. We are looking for someone who embraces the use of emerging technology to design, build, deploy, and govern AI solutions.\n\nKey Responsibilities:\nDesign, develop, and deploy scalable, cloud-native data engineering solutions on Azure using Databricks, ADLS Gen2, and Snowflake.\nBuild and operationalize end-to-end data pipelines for batch and streaming workloads, enabling reliable ingestion, transformation, and consumption of structured and unstructured data.\nLeverage Generative AI and LLM capabilities to enhance data engineering workflows, including data ingestion automation, schema inference, data quality validation, metadata enrichment, and analytics enablement.\nImplement and manage modern data architectures such as Lakehouse patterns using Delta Lake, optimizing data reliability, performance, and governance.\nDevelop secure, scalable APIs and data services to expose curated datasets and GenAI-enabled insights to downstream analytics and applications.\nImplement CI/CD pipelines for data engineering workloads using Azure DevOps or GitHub, supporting automated testing, deployment, and environment promotion.\nEnsure data platform security and compliance by applying best practices such as encryption, access controls, role-based permissions, and secure data sharing.\nMonitor and troubleshoot data pipelines and platforms using tools such as Databricks monitoring, Azure Monitor, Log Analytics, and related observability solutions.\nCollaborate in an Agile/Scrum environment with data scientists, analytics engineers, architects, and business stakeholders to deliver end-to-end data solutions.\nCreate and maintain comprehensive documentation, including data models, pipeline designs, operational runbooks, and GenAI integration guidelines.\nApply responsible and ethical AI principles when enabling GenAI use cases on data platforms, including data quality, lineage, transparency, and bias awareness.\nStay current with advancements in data engineering, Databricks, Snowflake, cloud-native data services, and emerging GenAI patterns relevant to analytics and data platforms.\n\nRequired Qualifications:\nBachelor's degree in computer science, Engineering, or a related field.\n7+ years of hands-on Data Engineering development experience with production deployments.\nStrong proficiency in Python, SQL, and modern software engineering practices.\nExperience with Azure AI Foundry, Azure OpenAI, Azure AI Search, and core Azure cloud services.\nExpertise in version control (Git), CI/CD pipelines for ML, and cloud DevOps workflows.\nPractical experience with data engineering tools and scalable data processing (e.g., Databricks, PySpark).\nStrong communication, analytical thinking, and problem-solving abilities.\nKnowledge of security best practices for AI systems and responsible AI considerations.\n\nNice-to-Have Skills:\nExperience with LLM application frameworks such as LangChain, Semantic Kernel, and LlamaIndex.\nExperience with multimodal AI use cases and advanced GenAI workloads.\nKnowledge of feature stores and ML operations tools such as Feast, Tecton, Evidently.\nAzure Cloud Data Engineering [ADF, Databricks, Logic App, ADLS, DevOps etc].\nSkills\nAzure, Data Engineering, Databricks, PySpark, Python, Snowflake","description_format":"text","description_chars":5138,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Commerce","Retail & Shop Systems","Apparel & Fashion Brands"],"lifecycle":[{"event":"open","at":"2026-11-10T17:35:30Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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