Job Title: Data Engineer

Location: Hyderabad / Bengaluru

Job Type: Full-time

Experience Level: Associate / Mid-Level (2–3 Years)

Role Overview

We are seeking a motivated Multi-Cloud Data Engineer to join our enterprise data infrastructure team. In this role, you will assist in building, optimizing, and maintaining our hybrid, multi-cloud data pipelines and Data Lakehouse environments across both Google Cloud Platform (GCP) and Microsoft Azure.

Key Responsibilities

- Pipeline Maintenance & Ingestion: Assist in designing, building, and maintaining batch and streaming data pipelines to move and transform data seamlessly across GCP and Azure environments.
- Lakehouse Support: Support the development of our unified Data Lakehouse architecture using open storage formats (e.g., Delta Lake, Apache Iceberg) across Azure Data Lake Storage (ADLS) and Google Cloud Storage (GCS).
- Data Processing & Compute: Execute Apache Spark data transformation tasks using managed cloud services such as Azure Databricks and GCP Dataproc/Dataflow under senior guidance.
- Data Warehousing & Optimization: Write efficient SQL queries, support data loading, and apply partitioning and clustering techniques within Google BigQuery and Azure Synapse Analytics / Fabric.
- Data Modeling & Validation: Implement dimensional data models (star/snowflake schemas) and execute data validation and quality checks across multi-cloud analytical layers.
- Orchestration & Troubleshooting: Monitor workflows and schedule DAGs using Apache Airflow (GCP Cloud Composer) or Azure Data Factory (ADF),



proactively resolving pipeline failures.

Required Qualifications & Skills

Experience & Cross-Cloud Expertise

- 2–3 years of hands-on experience in data engineering, with exposure to building or supporting data solutions in GCP and/or Azure environments.
- Certifications (Plus): Associate Cloud Engineer or Data Engineer certifications (GCP or Azure) are highly preferred.

Technical Deep Dive

- Google Cloud Ecosystem: Deep proficiency with BigQuery, Dataproc, Cloud Composer, Pub/Sub, and Cloud Storage.
- Microsoft Azure Ecosystem: Deep proficiency with Azure Databricks, Azure Data Factory (ADF), Synapse Analytics, Event Hubs, and ADLS Gen2.
- Big Data Frameworks: Advanced knowledge of Apache Spark (PySpark/SQL) optimized for cross-cloud clusters.
- Modern Storage Formats: Proven experience utilizing Delta Lake, Iceberg, or BigLake tables for multi-cloud Lakehouse consistency.
- Languages & Tools: Strong proficiency in SQL and Python. Familiarity with dbt, Git, and CI/CD version control workflows.

Soft Skills & Multi-Cloud Perspective

- Cost Observability Mindset: Highly focused on predicting, tracking, and minimizing cross-cloud egress fees and processing overhead.
- Architectural Flexibility: Ability to match the right workload to the right cloud setting based on performance, cost, and native feature benefits.
- Collaborative Translator: Strong communication skills to act as the primary bridge between disparate GCP-focused and Azure-focused infrastructure teams.

📌 Data Engineer (Hyderabad)
🏢 Brio Technologies
📍 Hyderabad