Senior Technical Specialist

HCL Technologies Limited; Hyderabad; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

HCL Technologies Limited in Hyderabad is seeking a senior/medior Data Engineer to design, build, and operate scalable data services supporting analytics, AI, and GenAI use cases across business domains.

You will work hands-on with data pipelines, data models, orchestration frameworks, and observability tooling, collaborating with AI Engineers, Data Scientists, and Platform teams to deliver self-service data products.

Qualifications

• 5+ years of experience in data engineering and building production-grade data pipelines.
• Strong hands-on experience with data platforms such as Databricks.
• Solid knowledge of data modeling, SQL, Spark, and Python.
• Experience with orchestration frameworks, data quality tooling, and observability practices.
• Exposure to unstructured data processing and AI/GenAI data pipelines is a strong plus.

Responsibilities

• Build and maintain scalable data pipelines and ingestion frameworks for batch, streaming, and event-driven data.
• Develop and maintain modular data models and semantic layers optimized for analytics, BI self-service and AI use cases.
• Implement and operate orchestration workflows and compute engines (Spark, SQL, Python).
• Work with storage technologies such as Delta Lake, ADLS, feature and vector stores.
• Deliver curated datasets and reusable data assets for analytics, machine learning, and GenAI applications.

Skills

Data pipelines

Data modeling

SQL

Python

Databricks

Spark

Orchestration

Observability tooling

Tools

dbt

Databricks Lakeflow

Job description

Job Title: Data Engineer Department: Global Analytics Reports to: Manager AI Engineering Level: Senior / Medior (depending on experience) Role Summary The Data Engineer is responsible for designing, building, and operating high-quality, scalable, and reusable data services that support analytics, AI, and GenAI use cases across business domains. In this role, you will design and work hands-on with data pipelines, data models, orchestration frameworks, storage layers, and observability tooling. You will collaborate closely with AI Engineers, Data Scientists, Product Owners, and Platform teams to deliver reliable, well-governed, and self-service data products.

Key Responsibilities

Key Responsibilities Data Platform & Services Engineering

• Build and maintain scalable data pipelines and ingestion frameworks for batch, streaming, and event-driven data.
• Develop and maintain modular data models and semantic layers optimized for analytics, BI self-service and AI use cases.
• Implement and operate orchestration workflows (e.g., Databricks Workflows) and compute engines (Spark, SQL, Python).
• Work with storage technologies such as Delta Lake, ADLS, feature and vector stores.

Data Quality, Governance & Observability

• Implement data quality checks, validations, and monitoring to ensure reliability and trust in data products.
• Contribute to data lineage, metadata management, and documentation.
• Apply observability practices using tools such as Great Expectations or Monte Carlo.
• Ensure compliance with data governance standards and regulations (e.g., GDPR) in collaboration with data governance teams.

Enablement for AI & Analytics Use Cases

• Deliver curated datasets and reusable data assets for analytics, machine learning, and GenAI applications.
• Build pipelines that process structured, graph, and unstructured data (e.g., text, documents, images).
• Support AI Engineering teams with data preparation for embeddings, vector stores, and retrieval-augmented generation (RAG) pipelines.

Tooling & Self-Service

• Contribute to data engineering tooling and frameworks that enable e icient development and deployment of pipelines.
• Develop data pipelines using tools such as dbt and Databricks Lakeflow.
• Support reuse of data services through clear documentation, data contracts, templates, and examples.

Collaboration & Ways of Working

• Collaborate with Data Scientists, AI Engineers, Product Owners, Business SMEs, and Platform teams.
• Participate in technical design discussions, code reviews, and architecture forums.
• Follow engineering best practices for version control, testing, CI/CD, and operational excellence.

Skill Requirements

Preferred Qualifications

• 5+ years of experience in data engineering and building production-grade data pipelines.
• Strong hands-on experience with data platforms such as Databricks.
• Solid knowledge of data modeling, SQL, Spark, and Python.
• Experience with orchestration frameworks, data quality tooling, and observability practices.
• Exposure to unstructured data processing and AI/GenAI data pipelines is a strong plus.
• Experience working in a global, multi-team environment is beneficial.

Other Requirements

Success in This Role Means

• Reliable, well-documented data products are available for analytics and AI use cases.
• Data pipelines are scalable, cost-e icient, observable, and easy to operate.
• Data engineers and AI teams can move faster using reusable patterns and self service data services.
• Structured and unstructured data are e ectively integrated to support advanced analytics and GenAI innovation.

At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first.

HCLTech is a global technology company, home to more than 223,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending June 2026totaled $14.8billion.