ML ETL Architect

ML Data Architect
Location: Culver City, CA
Duration: 6 months
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Seeking a talented Machine Learning / ETL Engineer to join our IT team and take on the challenge of advancing our Business Intelligence initiatives. In this role, you will be responsible for designing, developing and optimizing ETL / ELT processes for large-scale data processing and leveraging Machine Learning frameworks to perform data quality and anomaly detection across 1st and 3rd party datasets.

Responsibilities-
Architecting and implementing robust, high-performance data pipelines across various data sources (databases, APIs, web services) to efficiently process and transform large datasets.
Enable and ensure data collection from various sources and loading it to a data lake, data warehouse, or any other form of storage.
Proficiently utilizing cloud platforms Snowflake, AWS, Google Cloud Platform to build scalable data processing solutions, including managing data lakes and data warehouses.
Developing and implementing data quality checks and monitoring systems to ensure data accuracy and reliability throughout the data pipeline.
Contribute to data documentation and governance strategies, including data access controls, data lineage tracking, and data retention policies.

Qualifications:-
10+ years of experience with data warehouse technical architectures, ETL/ ELT, reporting/analytic tools, and scripting.
Extensive knowledge and understanding of data modeling, schema design, and data lakes.
5+ years of data modeling experience and proficiency in writing advanced SQL and query performance tuning with on Snowflake in addition to Oracle, and Columnar Databases SQL optimization experience.
Experience with AWS services including S3, Lambda, Data-pipeline, and other data technologies.
Experience implementing Machine Learning algorithms for data quality, anomaly detection and continuous monitoring, etcs.
Required skills and experience:
Strong proficiency in Python, SQL for data manipulation and processing.
Experience with data warehouse solutions for Snowflake, BigQuery, Databricks.
Ability to design and implement efficient data models for data lakes and warehouses.
Familiarity with CI/CD pipelines and automation tools to streamline data engineering workflows
Deep understanding of principles in data warehousing and cloud architecture for building very efficient and scalable data systems.
Experience with Apache Airflow and/or AWS MWAA