Why Build Your Career at NICE?
At NICE, careers are built with intention combining cutting-edge
technology, continuous learning, and global exposure.
Impactful Innovation
Design and deliver enterprise-grade
Data Engineering, Analytics, BI
Reporting, and AI solutions that
power real-world decision
intelligence across industries.
Structured Career Growth
Role-based growth paths, certifications, leadership programs, and continuous upskilling.
Certification reimbursement programs
Planned progression, not guesswork
Global Client Exposure
Work with 100+ global clients across India, Middle East, USA, and beyond spanning BFSI, Retail, Media, Automotive, and Life Sciences.
On-Site & Consulting Opportunities
Gain hands-on exposure at client locations to strengthen consulting, communication, and solution architecture skills. Additionally, contribute to Centers of Excellence (CoEs) and innovation initiatives.
Empowered Work Culture
A collaborative, inclusive environment where ownership is encouraged and ideas are valued:
Mentorship from senior architects & practitioners
Flat, transparent communication
Rewards & Recognition
Spot awards, peer recognition, performance bonuses, and milestone celebrations that acknowledge impact—not just tenure.
A Vibrant Professional Community
Hackathons, CSR initiatives, sports, learning forums, and team celebrations that foster belonging and collaboration.
Perks & Benefits
Benefits That Support Your Professional and Personal Life
Fitness & wellness partnerships
Local restaurant & lifestyle tie-ups
Clearly defined career progression paths
Upskilling & Cross-skilling programs
Flexible Work Hours
Sustainable work–life balance
Health & accident insurance
Upskilling & Cross-skilling programs
AI Data Engineer
- FullTime
- EXP : 5-8 Years
- Pune/Nagpur , India
Job Summary
We are hiring an AI Data Engineer with 5–8 years of experience in Azure Data Engineering. The ideal candidate should have hands-on expertise in Azure Data Factory, Azure Databricks, ADLS Gen2, Python, SQL, PySpark, and ETL/ELT pipelines, with exposure to Azure Synapse/Microsoft Fabric and AI/GenAI technologies such as Azure OpenAI.
Key Responsibilities
Core Data Engineering Skills
- Good hands-on experience with Python for data processing, automation scripts, API integration, basic logging, and reusable data engineering components.
- Strong SQL and T-SQL skills, including joins, CTEs, stored procedures, window functions, query tuning, and analytical data preparation.
- Working experience with PySpark or Spark for data transformation, large dataset handling, partitioning, and performance optimization.
- Experience designing and implementing batch and incremental ETL/ELT pipelines for structured, semi structured, and unstructured data.
- Data ingestion experience from relational databases, files, REST APIs, SaaS platforms, event sources, and enterprise applications.
- Good understanding of data modeling, data warehousing, lakehouse concepts, dimensional modeling, star schema, and medallion architecture.
- Practical exposure to Parquet, Delta Lake, data quality checks, schema handling, validation, and reconciliation activities.
- Ability to troubleshoot production data issues, optimize pipelines, resolve failures, and support business critical data workloads.
Microsoft Azure Data Platform Skills
- Hands-on experience with key Microsoft Azure data services such as Azure Data Factory, Azure Databricks, Azure Data Lake Storage Gen2, Azure SQL Database, and Azure Synapse Analytics.
- Experience building orchestration pipelines using Azure Data Factory or Synapse Pipelines, including linked services, datasets, triggers, parameters, integration runtime, and monitoring.
- Practical experience with Azure Databricks notebooks, jobs, clusters, and Spark-based transformation workloads.
- Exposure to Azure Synapse or Microsoft Fabric for lakehouse, warehouse, SQL analytics, notebooks, or curated datasets is preferred.
- Basic understanding of Microsoft Fabric components such as OneLake, Lakehouse, Warehouse, Data Factory pipelines, notebooks, and semantic models is an added advantage.
- Experience with Power BI data enablement, including curated data models, refresh-ready datasets, performance-oriented tables, and business reporting integration.
AI, GenAI and Machine Learning Exposure
- Practical exposure to integrating AI or GenAI capabilities into data solutions using Azure AI Services, Azure OpenAI Service, or similar API-based services.
- Understanding of common AI-enabled use cases such as document processing, summarization, classification, intelligent search, chatbots, and workflow automation.
- Basic working knowledge of prompt engineering, embeddings, vector search, retrieval-augmented generation, semantic search, and grounding AI outputs on enterprise data.
- Exposure to Azure AI Search, Document Intelligence, OpenAI model APIs, model endpoints, token usage, and API-based AI integration is preferred.
- Basic understanding of ML lifecycle concepts such as feature engineering, model usage, deployment support, monitoring, versioning, and responsible AI practices.
- Exposure to Python AI/ML libraries or frameworks such as pandas, scikit-learn, MLflow, LangChain, Semantic Kernel, or equivalent tools is good to have.
DevOps, Security and Governance Awareness
- Working knowledge of Git, Azure DevOps, branching, pull requests, code reviews, CI/CD basics, and deployment support for data engineering components.
- Awareness of environment management across development, test, staging, and production environments.
- Understanding of Azure security concepts including Key Vault, managed identities, RBAC, private endpoints, network security, secrets management, and secure data access.
- Awareness of data governance, lineage, cataloging, access controls, auditability, privacy, and compliance practices using Microsoft Purview or equivalent tools.
- Familiarity with monitoring and observability using Azure Monitor, Log Analytics, pipeline alerts, job monitoring, and operational dashboards
Required Skills & Qualifications
- 5–8 years of overall data engineering experience, with hands-on delivery experience on Microsoft Azure data platforms.
- Minimum 2–4 years of practical experience with Azure Data Factory, Azure Databricks, ADLS Gen2, SQL, Python, and PySpark.
- Exposure to at least 1–2 AI, GenAI, ML, automation, or intelligent data use cases is preferred; production experience is an advantage but not mandatory.
Good to have
- Exposure to Kafka, Azure Event Hubs, Azure Stream Analytics, Databricks Structured Streaming, or other real-time data processing technologies.
- Exposure to Microsoft Fabric migration, Synapse-to-Fabric transition, lakehouse modernization, or Power BI semantic model optimization.
- Basic exposure to Azure Functions, Logic Apps, API Management, containers, or serverless data workflows.
- Relevant Microsoft certifications such as Azure Data Engineer Associate, Fabric Analytics Engineer Associate, or Azure AI Engineer Associate are preferred but not mandatory.
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