Benefits:
Company parties
Competitive salary
Dental insurance
Free food & snacks
Hi
Hope doing good & Well
Work location: Dallas, TX /Atlanta, GA
End client: IBM/DTV
Compltely on site position
Job Description
We are looking for an experienced Databricks Subject Matter Expert (SME) with expertise in Data Profiling and Data Modeling to join our growing team. In this role, you will be responsible for leveraging Databricks to drive end-to-end data solutions, ensuring data quality, and optimizing data pipelines for performance and scalability. You will also play a pivotal role in designing, implementing, and maintaining data models that align with business requirements and industry best practices.
The ideal candidate should have deep experience with the Databricks ecosystem, including Spark, Delta Lake, and other cloud-based data technologies, combined with a strong understanding of data profiling and data modeling concepts. You will collaborate closely with data engineers, data scientists, and business analysts to ensure data integrity, accuracy, and optimal architecture.
Skills and Qualifications:
Technical Skills:
Databricks (including Spark, Delta Lake, and other relevant components)
· 8+ years of hands-on experience with Databricks or related technologies.
Strong expertise in Data Profiling tools and techniques.
Experience in Data Profiling and Data Quality management.
· Experience in Data Modeling, including working with dimensional models for analytics.
Advanced knowledge of SQL, PySpark, and other scripting languages used within Databricks.
Experience with Data Modeling (e.g., relational, dimensional, star schema, snowflake schema).
Hands-on experience with ETL/ELT processes, data integration, and data pipeline optimization.
Familiarity with cloud platforms (AWS, Azure, Google Cloud) and cloud data storage technologies.
Proficiency in Python, Scala, or other programming languages commonly used in Databricks.
Experience in Data Governance and Data Quality practices..
· Familiarity with Machine Learning workflows within Databricks is a plus.
Thank you
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