Must Have:

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  Databricks and Snowflake for data platforms
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  Spark or PySpark with Python for batch processing
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  Advanced SQL with query tuning, partitioning, clustering
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  Data modeling using star, snowflake, SCD, OBT, normalized models
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  Experience with Medallion architecture
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  Data ingestion pipelines and large-scale migrations
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  Orchestration tools for data workflows
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  Data platform debugging and observability

Responsibilities:

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  Design and build large-scale data pipelines for ingestion and transformation
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  Develop ETL and ELT frameworks using Databricks and Spark
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  Optimize SQL queries and improve data performance
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  Build and maintain scalable data models across lakehouse platforms
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  Lead data migration efforts across systems and environments
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  Implement orchestration for reliable data workflows
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  Monitor data pipelines and resolve production issues
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  Ensure governance, data quality, and observability across platforms

Qualifications:

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  7+ years in data engineering or data platform roles
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  Strong hands-on experience with Databricks or Snowflake
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  Deep expertise in SQL and distributed data processing
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  Experience building scalable data models and architectures
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  Proven experience with large-scale data migrations
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  Bachelor’s degree in Computer Science or related field

Nice to Have:

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  Experience with ML data pipelines and feature engineering
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  Exposure to streaming frameworks like Kafka
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  Knowledge of cloud platforms like AWS, Azure, or GCP
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  Experience with data governance tools and frameworks