Lead Data Engineer for Data Infrastructure
Краткое
Freelancer Client is hiring: Lead Data Engineer for Data Infrastructure.
Location: Remote
Compensation: ₹50,000 – ₹60,000/month
Experience: 7 – 10 Years
Hiring: Lead Data Engineer (Data Platforms & Analytics Infrastructure)
We are looking for a Lead Data Engineer to build and scale a robust data platform supporting analytics and AI/ML use cases. This role involves end-to-end ownership of the data lifecycle along with leading a team to deliver reliable and high-performance data solutions.
What you'll do:
• Build scalable batch and real-time data pipelines
• Design and implement Lakehouse architecture (Delta Lake / Iceberg)
• Develop ETL/ELT workflows using Airflow, dbt, or Prefect
• Ensure data quality, governance, and monitoring
• Design data models for analytics and ML use cases
• Optimize systems for performance, scalability, and cost
• Lead and mentor team members; enforce engineering best practices
• --
Requirements:
• 7–10 years in Data Engineering (3+ years in lead role)
• Strong in SQL, Python, and Spark (PySpark/Scala)
• Experience with Snowflake, Databricks, BigQuery, or Redshift
• Solid understanding of data modeling (Kimball / Data Vault)
• Hands-on with streaming systems (Kafka / Flink)
• Familiarity with Terraform, CI/CD, and Cloud (AWS/GCP/Azure)
• --
Nice to have:
• Contract Duration: 8 Months
• --
• Experience with Feature Stores (Feast, Tecton)
• Knowledge of Data Mesh / CDC tools (Fivetran, Airbyte)
• Exposure to Graph or Vector Databases
Skills: Python, SQL, Big Data Sales, Hadoop, SQLite, Data Warehousing, ETL, Apache Spark
Source: Freelancer Client via Remote / Online. Apply on the source website.
Оригинал
Hiring: Lead Data Engineer (Data Platforms & Analytics Infrastructure)
Job ID: GKS2005_1
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Role: Lead Data Engineer
Mode: Remote (Monthly visit to Coimbatore office preferred)
Budget: ₹50,000 – ₹60,000/month
Contract Duration: 8 Months
Experience: 7 – 10 Years
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About the Role
We are looking for a Lead Data Engineer to build and scale a robust data platform supporting analytics and AI/ML use cases. This role involves end-to-end ownership of the data lifecycle along with leading a team to deliver reliable and high-performance data solutions.
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Key Responsibilities
Build scalable batch and real-time data pipelines
Design and implement Lakehouse architecture (Delta Lake / Iceberg)
Develop ETL/ELT workflows using Airflow, dbt, or Prefect
Ensure data quality, governance, and monitoring
Design data models for analytics and ML use cases
Optimize systems for performance, scalability, and cost
Lead and mentor team members; enforce engineering best practices
---
Required Skills
7–10 years in Data Engineering (3+ years in lead role)
Strong in SQL, Python, and Spark (PySpark/Scala)
Experience with Snowflake, Databricks, BigQuery, or Redshift
Solid understanding of data modeling (Kimball / Data Vault)
Hands-on with streaming systems (Kafka / Flink)
Familiarity with Terraform, CI/CD, and Cloud (AWS/GCP/Azure)
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Good to Have
Experience with Feature Stores (Feast, Tecton)
Knowledge of Data Mesh / CDC tools (Fivetran, Airbyte)
Exposure to Graph or Vector Databases
Open-source contributions or advanced degree
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Tech Stack
Python, SQL, PySpark, Scala | Snowflake, Databricks, BigQuery
Airflow, dbt | Kafka | AWS/GCP | Docker, Kubernetes, Terraform
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Interested candidates can reach out directly:
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