Data Engineer – Azure Data Platforms

Arvillium; Pune District; Sur place; INR 1 200 000 - 2 400 000; Plein temps

Résumé du poste

Arvillium is seeking a Data Engineer in Pune, India to design and run modern cloud data solutions. You will work with data scientists and analysts to keep data flowing reliably across systems, building scalable pipelines on Databricks, Fabric and Azure Data Factory.

You will craft analytics-ready data models, manage Azure lakehouse storage, and implement monitoring, validation, and lineage across pipelines. Strong Python/SQL skills and a team-focused mindset are essential.

Qualifications

• Experience with Spark on Databricks and orchestration using Fabric or ADF.
• Proficient in Python and SQL.
• Knowledge of lakehouse design and Delta/Parquet data formats.
• Understanding of Azure security and access concepts.
• Ability to explain technical topics clearly and work in a team.

Responsabilités

• Engineer data workflows, both scheduled and real-time, across Databricks, Fabric and ADF.
• Shape raw data into layered analytics-ready models (bronze/silver/gold).
• Manage lake and warehouse storage on Azure, focusing on cost and efficiency.
• Build monitoring, validation and lineage into pipelines.
• Create reusable notebooks and frameworks to speed up delivery.
• Participate in automated deployments and act as escalation point for live data issues.

Connaissances

Spark on Databricks

Python

SQL

Git

Data modeling

Delta/Parquet

Azure security concepts

Outils

Databricks

Microsoft Fabric

Azure Data Factory

Description du poste

Build scalable data pipelines and platforms on Databricks, Microsoft Fabric and Azure Data Factory to power analytics, reporting and AI/ML.

About the Role

You’ll design and run modern cloud data solutions, from ingestion through transformation to serving. You’ll work closely with data scientists, analysts and platform teams to keep data flowing reliably and efficiently across systems.

Responsibilities

• Engineer data workflows, both scheduled and real-time, across Databricks, Microsoft Fabric and ADF
• Shape raw data into layered, analytics-ready models (bronze/silver/gold, dimensional)
• Manage lake and warehouse storage on Azure, with a focus on cost and efficiency
• Build monitoring, validation and lineage into every pipeline
• Create reusable notebooks and frameworks that speed up delivery across the team
• Take part in automated deployments and act as a point of escalation for live data issues

Requirements

• Practical, project-level experience with Spark on Databricks, plus Fabric or ADF orchestration
• Confident coder in Python and SQL
• Good working knowledge of lakehouse design, Delta/Parquet and data modelling approaches
• Awareness of Azure security and access concepts (identities, roles, private networking)
• Comfortable working with Git in a team setting, and able to explain technical topics clearly