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At Nat Habit, we are building efficacious personal care using natural ingredients while doing primary research to build formulations from a first principle basis to find cures, study the underlying impact on different markers and then conduct clinical studies to prove efficacy of products. Check us out on www.nathabit.in
The founding team has a strong startup experience and is well funded and backed by top angel investors and tier 1 institutional investors.
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:
Ideal candidate will have:
Do not apply if following is applicable to you:
Work
Location: Udyog Vihar - Sector 18, Gurgaon
www.nathabit.in
www.instagram.com/nathabit.in
Working Days: Mon - Sat (2nd/4th Sat are off)
Job ID: 151635307
Skills:
data engineering , snowflake , Github, Pyspark, Apache Spark, Sql, Bitbucket, Apache Kafka, Gitlab, Databricks, Python, Delta Lake on Databricks, Data Quality Validation
Skills:
data warehouses , snowflake , Power Bi, Data Modeling, Data Lineage, Jira, Sql, Dimensional Modeling, Data Visualization, Python, AWS, ELT patterns, Semantic layers, Data Validation, Analytics engineering, Data automation, Project Management, Version-controlled analytics code
Skills:
Java, Cassandra, Scala, PostgreSQL, Apache Spark, Kafka, Spring Boot, Sql, Apache Nifi, REST, Gcp, Docker, MongoDB, Oracle, Kubernetes, Python, AWS, Airflow, Flink, GRPC
Skills:
data engineering , Machine Learning, Artificial Intelligence, Apache Spark, Sql, Data Science, Git, Docker, Apache Kafka, Python, AWS, Etl, LangChain, Generative AI, LLMs, LlamaIndex
Skills:
Data Modeling, Sql, Spark, Data Integration, Python, data pipeline development, OneLake, schema management, data quality frameworks, Reconciliation, incremental processing, Pipelines, Notebooks, performance optimization, partitioning, Lakehouse architecture, Microsoft Fabric, Azure data services, Validation