Lead Data Engineer - R01571409

Brillio 2
Bangalore +1 more
On-site

Who this role is best for

A natural match if you have hands-on experience in building cloud data infrastructure with Snowflake, dbt, and AWS.

Best fit for

  • Candidates with 8-10 years of experience in data engineering and a track record of managing end-to-end cloud data pipelines
    — “8- 10 years of experience in data engineering”
  • Individuals who have led Medallion modeling efforts across Bronze → Silver → Gold layers
    — “Medallion modeling: Design, build, and maintain dbt models across Bronze → Silver → Gold layers”
  • Professionals with a background in data governance and alignment with certification standards
    — “Partner with the Data Governance team so models meet certification and quality-gate standards before promotion”

Things to consider

  • On-call support and troubleshooting responsibilities are expected as part of the role
    — “Troubleshoot and resolve production issues, conducting thorough root cause analysis”

How to stand out

  • Highlight your experience with Snowflake Time Travel and Fail Safe features in your resume and interviews
    — “Hands-on experience with Snowflake data warehousing, including Time Travel and Fail Safe features”
  • Emphasize your ability to design scalable ETL/ELT pipelines using dbt and SQL
    — “Develop, maintain, and optimize dbt models, macros, and tests to support scalable ETL/ELT data pipelines”
  • Showcase your work on CI/CD pipelines for data deployments and automation
    — “Implementation and maintenance of CI/CD pipelines for automated deployment and release management of data solutions”
  • Demonstrate your ability to document data lineage and model logic clearly
    — “Document data lineage, model logic, and key technical decisions so the work is maintainable by the internal team”
  • Demonstrate your familiarity with AWS services like S3, Lambda, and CloudWatch
    — “Management of AWS services such as S3, Lambda, Secret Manager, IAM, and CloudWatch”
Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • developed and maintained dbt models
  • optimized Snowflake performance
  • integrated AWS services
  • implemented CI/CD pipelines
  • configured monitoring and alerting systems
Typical background
8-10 years of experience in data engineeringadvanced proficiency in SQLexpertise in dbt and Snowflake

Skills & requirements

Required

Dbt ModelsSnowflake Data WarehousingAWS Cloud ServicesEtl/elt PipelinesCi/cd PipelinesData Quality ChecksTroubleshootingCollaboration With Analytics And Engineering TeamsMedallion ModelingDownstream SupportDocumentation

Preferred

Modern Data Platform FundamentalsOptimizing Large-scale Data PipelinesData GovernanceInfrastructure-as-code ToolsAdvanced Snowflake Features

Stack & domain

SnowflakedbtAWSSQLPythonETLELTS3LambdaSecret ManagerIAMCloudwatchCI/CDData QualityData GovernanceInfrastructure-as-codeData SharingSecure Data ExchangeCommunicationProblem-solvingTeamworkLeadershipCuriosityResourcefulnessProactivityCollaborationTechnologyData Engineering

About the role

Original posting from Brillio 2 via Lever

Lead Data Engineer

Job requirements:

Experience Range: 8- 10 years of experience in data engineering, with hands-on expertise in Snowflake, dbt, and AWS cloud services Key Responsibilities:

  • Develop, maintain, and optimize dbt models, macros, and tests to support scalable ETL/ELT data pipelines
  • Administer and manage Snowflake data warehouses, including databases, schemas, roles, and security configurations to ensure robust data governance
  • Optimize complex SQL queries and warehouse performance, reducing processing times and improving storage utilization
  • Manage and integrate AWS services such as S3, Lambda, Secret Manager, IAM, and CloudWatch to build and maintain reliable cloud data infrastructure
  • Implement and maintain CI/CD pipelines for automated deployment and release management of data solutions
  • Configure and monitor data quality checks, alerting systems, and performance monitoring to ensure high data reliability and integrity
  • Troubleshoot and resolve production issues, conducting thorough root cause analysis to minimize downtime and prevent recurrence
  • Collaborate closely with analytics, business intelligence, and engineering teams to deliver high-impact, scalable data solutions aligned with business objectives
  • Medallion modeling: Design, build, and maintain dbt models across Bronze → Silver → Gold layers for the assigned domain.
  • Governance alignment: Partner with the Data Governance team so models meet certification and quality-gate standards before promotion.
  • Downstream support: Support data modeling for downstream analytics platform consumers (dashboards and data products)
  • Troubleshooting & support: Diagnose and resolve pipeline issues; participate in on-call/support rotation as needed.
  • Documentation: Document data lineage, model logic, and key technical decisions so the work is maintainable by the internal team.
  • Maintain and Develop APIsRequired Skills:
  • Advanced proficiency in SQL (basic and advanced)
  • Expertise in developing and managing dbt models, macros, and tests
  • Hands-on experience with Snowflake data warehousing, including Time Travel and Fail Safe features
  • Strong understanding of ETL/ELT fundamentals and best practices
  • Proficiency in Python for data engineering and automation tasks
  • Administration of Snowflake warehouses, databases, schemas, and role-based security
  • Management of AWS services such as S3, Lambda, Secret Manager, IAM, and CloudWatch
  • Implementation and maintenance of CI/CD pipelines for data deployments
  • Configuration of monitoring, alerting, and data quality checks in cloud data platformsPreferred Skills:
  • Experience with modern data platform fundamentals and architecture
  • Expertise in optimizing large-scale data pipelines for performance and cost efficiency
  • Familiarity with data governance and compliance best practices in cloud environments
  • Knowledge of infrastructure-as-code tools for cloud resource management
  • Exposure to advanced Snowflake features such as data sharing and secure data exchangeDesired Qualifications:
  • Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related field
  • Relevant industry certifications in AWS, Snowflake, or dbt are highly desirable

Source: Brillio 2 careers (Lever)

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