Lead Data Infrastructure Engineer

2 days ago

Bengaluru, Karnataka, India beBeeDataEngineer Full time US$ 90,000 - US$ 1,20,000
Job Description:

We are seeking a seasoned data engineer to lead our end-to-end design and development of scalable data pipelines.

This individual will be responsible for orchestrating complex ELT workflows, architecting and optimizing data warehousing solutions, and building and maintaining AWS infrastructure.

Additionally, they will define and enforce data governance, security, and compliance policies across pipelines and AWS resources.

The ideal candidate will have 5+ years of data engineering experience, including extensive work with Talend and Snowflake in production environments.

Responsibilities:

  • Lead end-to-end design and development of scalable data pipelines using Talend, orchestrating complex ELT workflows into Snowflake.
  • Architect and optimize data warehousing solutions: warehouse sizing, clustering keys, materialized views, and Snowflake best practices.
  • Build and maintain AWS infrastructure (S3 data lakes, EC2 batch clusters, RDS, No SQL).
  • Define and enforce data governance, security, and compliance policies across pipelines and AWS resources.
  • Mentor and guide junior engineers, conduct design/code reviews, and establish coding standards.
  • Implement monitoring, alerting, and logging (Snowflake Usage & Billing) to ensure pipeline reliability and cost control.
  • Collaborate with analytics and BI teams to translate business requirements into technical designs and SLAs.
  • Drive continuous improvement: evaluate emerging tools (e.g., AWS Glue, dbt), recommend enhancements, and lead migration from legacy ETL systems.
Required Skills and Qualifications:

Skills & Qualifications:

  • 5+ years of data engineering experience, including extensive work with Talend and Snowflake in production environments.
  • Deep expertise in Studio, TAC and AWS services(S3, EC2, RDS and No SQL).
  • Expert SQL skills and performance tuning in Snowflake (query profiling, result caching, multi-cluster warehouses).
  • Proficient in Python and Java for advanced data transformations and custom connector development.
  • Experience with orchestration tools (Autosys, Airflow) and CI/CD for data pipelines.
  • Strong background in data modeling, schema design, and partitioning strategies for large-scale datasets.
  • Demonstrated ability to lead cross-functional projects, mentor teams, and communicate complex concepts to stakeholders.
  • Knowledge of data security, GDPR/CCPA compliance, and implementing role-based access controls.

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