Databricks Certified

Title: Databricks Certified - Data Architect and Machine Learning

Location: Remote (working hours as per EST time zone)

Position Type: Contract position

Responsibilities:

Databricks Certified Machine Learning Professional is required

Preferred to have current credentials or ability to obtain within first 60 days of employment

5+ years of experience with a proven experience in ETL testing

Hands-on experience in SQL with advanced level skills

Experience in scripting language (shell/python)

Knowledge and understanding in Data Warehouse and data modeling

Experience in healthcare insurance domain would be nice to have

Experience using Data Warehousing and Business Intelligence tools such as Datastage / Informatica, GCP Bigquery, Tableau and SSRS, etc.

Willingness to continuously learn & share learnings with others and capability to collaborate with stakeholders and project leaders to understand requirements, deliverables

Experience working in an agile and collaborative team environment

Excellent written and verbal communication, presentation and professional speaking skills

Proven problem-solving skills and attention to detail with a commitment to excellence and high Standards

Required Skills and Abilities:

5+ years or more consulting experience working with external clients across a variety of industry markets

7+ years’ experience in Data Architecture, data engineering & analytics in areas such as performance tuning, pipeline integration & infrastructure configuration

In-depth knowledge of AWS data services and related technologies, including but not limited to: Redshift, Glue, S3, AuroraDB, MWAA, Lambda, and SageMaker

Deep knowledge and expertise in Databricks and its components, such as Unity Catalog, Delta Lake, Delta Live Tables, Apache Spark, and related technologies such as dbt.

Production-level experience with data ingestion, streaming technologies (i.e., Kafka), performance tuning, troubleshooting, and debugging

Deep understanding of machine learning algorithms, techniques, and methodologies, with hands-on experience in applying supervised, unsupervised, and deep learning techniques to real-world problems.

Proficiency in programming languages such as Python, R, Spark, or Scala, including expertise in data manipulation and analysis libraries (e.g., NumPy, pandas).

Possess deep knowledge and hands-on experience with data streaming technologies, such as Apache Kafka, Apache Flink, or similar platforms.

Experience implementing real-time data processing pipelines for streaming analytics.

Experience with Terraform, Git, CI/CD tools, as well as Automation and Integration testing

Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and their machine learning services - preferred AWS

Possess strong problem-solving and analytical skills, with the ability to identify and resolve complex data engineering challenges.

Has excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and stakeholders.

Has the ability to adapt to changing project requirements and manage multiple tasks simultaneously.