Apache Spark Technical Specialist

Experience: 5 to Not Available years

Location: Amangal, India

Skills: data engineering, data pipelines, data models, orchestration frameworks, storage layers, observability tooling, Databricks, SQL, Python, Delta Lake, ADLS, data quality checks, metadata management, Great Expectations, Monte Carlo, GDPR, machine learning, GenAI, dbt, Databricks Lakeflow

Job Summary
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 efficient 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.
• Robust 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.
Success in This Role Means
• Reliable, well-documented data products are available for analytics and AI use cases.
• Data pipelines are scalable, cost-efficient, 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 effectively integrated to support advanced analytics and GenAI innovation.
Other Requirements
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📌 Apache Spark Technical Specialist (Mumbai)
🏢 HCL Technologies
📍 Mumbai