Senior Data Engineer

Nat Habit; Gurugram District; On-site; INR 900,000 - 1,700,000; Full time

Job summary

Nat Habit is seeking a Data Engineer to design, develop, deploy, and maintain reliable data pipelines that power business analytics and intelligence. The role emphasizes building production-grade data systems and scalable data platforms, with a focus on problem solving and clean code.

This position is ideal for engineers who enjoy end-to-end data pipeline ownership, data governance, and working with modern data stacks.

Qualifications

• Experience designing production-grade data pipelines and platforms.
• Strong SQL and Python development skills for data engineering tasks.
• Familiarity with data governance, observability, and data lineage practices.

Responsibilities

• Design, build and maintain scalable ETL/ELT pipelines using SQL, Python and tools such as Dagster, Airflow or dbt to ingest, transform and load data into data platforms.
• Develop scalable and reliable data ingestion workflows.
• Build incremental data pipelines capable of handling millions of records efficiently.
• Design reporting datasets and analytical data models.
• Work extensively with ClickHouse or other OLAP database for analytical workloads.
• Optimize query performance, data storage and processing efficiency through advanced SQL techniques, partitioning, indexing strategies and workload optimization.
• Monitor pipeline execution, troubleshoot failures, and improve system reliability.
• Solid understanding of batch and streaming data processing techniques.
• Define and enforce data governance policies including data security, access control, masking and lifecycle management within data platforms.

Skills

Python

Pandas

NumPy

PySpark

ETL/ELT

Data governance

Observability

Version control

Tools

Dagster

Airflow

dbt

Pentaho

Docker

Podman

Terraform

ClickHouse

Redshift

Git

Job description

Job Summary

Nat Habit is looking for a Data Engineer who enjoys building production-grade data systems. Youwill be responsible for designing, developing, deploying, and maintaining reliable data pipelines thatsupport business analytics and intelligence.

This role is ideal for engineers who enjoy solving real engineering problems, writing clean code,and building scalable data platforms.

Key Responsibilities

• Design, build and maintain scalable ETL/ELT pipelines using SQL, Python and tools such as Dagster, Airflow or dbt to ingest, transform and load data into data platforms.
• Develop scalable and reliable data ingestion workflows.
• Build incremental data pipelines capable of handling millions of records efficiently.
• Design reporting datasets and analytical data models.
• Work extensively with ClickHouse or other OLAP database for analytical workloads.
• Optimize query performance, data storage and processing efficiency through advanced SQL techniques, partitioning, indexing strategies and workload optimization.
• Monitor pipeline execution, troubleshoot failures, and improve system reliability.
• Solid understanding of batch and streaming data processing techniques.
• Define and enforce data governance policies including data security, access control, masking and lifecycle management within data platforms.

Ideal candidate will have:

• Strong proficiency in Python, including Pandas, NumPy, PySpark.
• Hands on experience with ETL / ELT and data integration tools, such as Apache Airflow, Pentaho, or Dagster
• One or more years of experience with RDBMS and OLAP databases like Clickhouse, Redshift.
• Good working knowledge of containers like Docker or Podman
• Experience working with IaaC like terraform
• Experience in data warehousing & data archival.
• Should have good understanding & knowledge about observability.
• Experience in version control tool: Git

Do not apply if following is applicable to you:

• Backend engineers who only enjoy request/response APIs and CRUD, and don't want to own end-to-end data pipelines, scheduling, and reliability.
• Engineers who do not have experience with business intelligence and related tooling.
• Data analysts who mainly write ad-hoc SQL and build dashboards, and do not have engineering experience eg: version control, testing, CI/CD, and data infrastructure.
• People who don't naturally question data quality - if obviously wrong or inconsistent metricsdon't bother you, this won't be a good fit.

Work

www.nathabit.in

www.instagram.com/nathabit.in

Working Days: Mon - Sat (2nd/4th Sat are off)