Lead Data Engineer - R01553055

Brillio

Bengaluru

On-site

INR 1,500,000 - 2,500,000

Full time

14 days+

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Benefits offered by this job

Competitive compensation and benefits
Remote flexibility
Growth opportunities

Job summary

An innovative technology company is seeking a Lead Data Engineer to spearhead cloud modernization initiatives in Bengaluru. The ideal candidate will possess deep expertise in Google Cloud Platform and BigQuery, with experience in developing scalable data pipelines and migrating data to the cloud. This role offers a competitive salary, remote flexibility, and opportunities for career growth in a collaborative environment.

Qualifications

  • 5+ years in Data Engineering with a focus on cloud technologies.
  • Minimum 2+ years of hands-on experience with GCP, specifically BigQuery.
  • Strong development experience with Apache Airflow, Python, and Apache Spark.

Responsibilities

  • Lead the migration of large-scale datasets to Google BigQuery.
  • Integrate data from various sources and build real-time streaming pipelines.
  • Modernize legacy SSIS packages into cloud-native ETL pipelines.

Skills

GCP
BigQuery
Python
Airflow
SQL
DBT

Job description

Lead Data Engineer


Primary Skills
  • GCP, Big Query, Python, Airflow, SQL, DBT
Job requirements
  • About the Role
  • We are seeking a Senior Data Engineer with deep expertise in Google Cloud Platform (GCP) and BigQuery to lead cloud modernization initiatives, develop scalable data pipelines, and enable real-time data processing for enterprise-level systems. This is a high-impact role focused on driving the transformation of legacy infrastructure into a robust, cloud-native data ecosystem.
  • Key Responsibilities
  • 1. Data Migration & Cloud Modernization
  • Analyze legacy on-premises and hybrid cloud data warehouse environments (e.g., SQL Server).
  • Lead the migration of large-scale datasets to Google BigQuery.
  • Design and implement data migration strategies ensuring data quality, integrity, and performance.
  • 2. Data Integration & Streaming
  • Integrate data from various structured and unstructured sources, including APIs, relational databases, and IoT devices.
  • Build real-time streaming pipelines for large-scale ingestion and processing of IoT and telemetry data.
  • 3. ETL / Data Pipeline Development
  • Modernize and refactor legacy SSIS packages into cloud-native ETL pipelines.
  • Develop scalable, reliable workflows using Apache Airflow, Python, Spark, and GCP-native tools.
  • Ensure high-performance data transformation and loading into BigQuery for analytical use cases.
  • 4. Programming & Query Optimization
  • Write and optimize complex SQL queries, stored procedures, and scheduled jobs within BigQuery.
  • Develop modular, reusable transformation scripts using Python, Java, Spark, and SQL.
  • Continuously monitor and optimize query performance and cost efficiency in the cloud data environment.
  • Required Skills & Experience
  • 5+ years in Data Engineering with a strong focus on cloud and big data technologies.
  • Minimum 2+ years of hands-on experience with GCP, specifically BigQuery.
  • Proven experience migrating on-premise data systems to the cloud.
  • Strong development experience with Apache Airflow, Python, and Apache Spark.
  • Expertise in streaming data ingestion, particularly in IoT or sensor data environments.
  • Strong SQL development skills; experience with BigQuery performance tuning.
  • Solid understanding of cloud architecture, data modeling, and data warehouse design.
  • Familiarity with Git and CI/CD practices for managing data pipelines.
  • Preferred Qualifications
  • GCP Professional Data Engineer certification.
  • Experience with modern data stack tools like dbt, Kafka, or Terraform.
  • Exposure to ML pipelines, analytics engineering, or DataOps/DevOps methodologies.
  • Why Join Us?
  • Work with cutting-edge technologies in a fast-paced, collaborative environment.
  • Lead cloud transformation initiatives at scale.
  • Competitive compensation and benefits.
  • Remote flexibility and growth opportunities.

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