Role & Responsibilities :

Key Responsibilities :

- Architect and implement enterprise-grade Lakehouse solutions using Databricks.

- Design and deliver scalable batch and real-time data pipelines using Apache Spark (PySpark/SQL).

- Build ETL/ELT pipelines, incremental data loads, and metadata-driven ingestion frameworks.

- Implement and optimize Databricks components: Delta Lake, Delta Live Tables, Autoloader, Structured Streaming, and Workflows.

- Design large-scale data warehousing solutions with 3NF and dimensional modeling.

- Establish data governance, security, and data quality frameworks, including Unity Catalog.

- Lead ML lifecycle management using MLflow and drive AI use cases (RAG, AI/BI).

- Manage cloud-native deployments on Microsoft Azure and integrate with enterprise systems (e.g., ServiceNow).

- Drive CI/CD, DevOps practices, and performance optimization of Spark workloads.

- Provide technical leadership, mentor teams, and ensure successful delivery.

- Collaborate with stakeholders to translate business requirements into scalable solutions.

Ideal Candidate :

- Strong Databricks Architect Profile with end-to-end Lakehouse ownership.

- Mandatory (Experience 1) : Must have 10+ years of software engineering experience with at least 5+ years in Data Engineering with hands on exposure to Databricks and strong ownership of end-to-end data pipeline development.

- Mandatory (Experience 2) : Must have at least 5+ years of expertise across the Databricks ecosystem Delta Lake, Delta Live Tables, Autoloader, Structured Streaming, Workflows, Unity Catalog.

- Mandatory (Tech skill 1) : Must have worked at architecture level, owning end-to-end design through deployment.

- Mandatory (Tech skill 2) : Must have strong experience with Python and SQL for data processing and Apache Spark for performance tuning & scalability.

- Mandatory (Tech skill 3) : Must have experience in large-scale data warehousing & advanced data modeling (3NF and dimensional) across batch and real-time systems.

- Mandatory (AI Exposure) : Must have at least a basic working understanding of how AI services or tools work.

- Mandatory (Communication) : Must have strong stakeholder management & requirement-gathering experience with US or UK clients.

- Mandatory (Company) : Must come from a B2B IT services or IT consulting background.

- Mandatory (Note 1) : CTC is inclusive of 5% variable.

- Preferred (Tech skill 1) : Azure Databricks or Azure data services experience (project runs on Azure DevOps).

- Preferred (Tech skill 2) : MLflow or MLOps practices and AI use cases (RAG, AI/BI).

- Preferred (Tech skill 3) : CI/CD, Databricks Asset Bundles (DABs) or equivalent packaging, Terraform or IaC, reusable deployment templates.

- Preferred (Integrations) : ServiceNow or enterprise system integrations.

- Preferred (Certifications) : Databricks (Data Engineer Associate or Professional, ML or GenAI tracks), Azure or AWS cloud certifications.

Perks, Benefits and Work Culture :

- The company provides free AWS and Azure certification training.

- Group medical insurance of 5 lakhs is included in the benefits package, along with meal allowances.