Live opening · Posted 7 days ago
Databricks Engineer
Cogniify · Bengaluru, Karnataka, India (Hybrid)
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At a glance
The key details from the original listing.
CompanyCogniify
LocationBengaluru, Karnataka, India (Hybrid)
Work modeHybrid
Source
Linkedin
Listed7 days ago
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About the role
Description supplied by the original job listing.
Role : Databricks Engineer
Panindia - Multiple locations- hybrid at Hyderabad, Pune, Bengaluru, Chennai, Goa, Gurugram, etc..
Fulltime with Cogniify
Please provide the following details along with an updated resume at parveza@cogniify.ai
Skills:
Databricks:
Python. pyspark coding:
Advanced SQL:
The Role
Must-Have Skills (Non-Negotiable)
Databricks: hands-on architecture and engineering experience on the Databricks Lakehouse Platform (production-grade, at scale)
Python: strong professional proficiency for data engineering and pipeline development
SQL: advanced proficiency for data modeling, transformation, and performance tuning
Candidates without demonstrable, hands-on experience in all three of the above will not be considered.
What We're Looking For
Bachelor's, Master's, or equivalent professional experience in Computer Science, Data Science, Statistics, or a related field.
6–9 years of professional experience in data engineering, analytics engineering, or data architecture, with demonstrated technical leadership on at least a few large-scale engagements.
Mandatory, hands-on expertise in Databricks, Python, and SQL in production environments.
Strong working knowledge of the modern data stack: dbt, Airflow/Dagster, Fivetran/Airbyte, Spark, Kafka, and cloud-native data services.
Solid grasp of data modeling methodologies (Kimball, Data Vault, Activity Schema, OBT) and the judgment to apply them across contexts.
Experience with cloud data infrastructure on AWS, Azure, or GCP (any combination is acceptable, alongside Databricks).
Proven ability to influence technical direction and align technical and business stakeholders on complex architecture topics.
Strong understanding of data governance, data quality, cataloging, lineage, and regulatory compliance frameworks.
Experience mentoring engineers and contributing to a high-performing data team.
Strong understanding of how data platforms serve AI/ML workloads, including feature engineering, vector data, and model input/output pipelines.
Work arrangement
Hybrid
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