Location: San Francisco, CA (Hybrid)
Job Type: Long-Term Contract-to-Hire
Pay Rate: $90/hour on 1099, all-inclusive
Work Arrangement: Hybrid
We are seeking a skilled Data Scientist with a strong Data Engineering background to design, develop, and optimize data pipelines, analytics solutions, and data-driven applications within the Google Cloud ecosystem.
The ideal candidate will combine data science and analytical skills with hands-on data engineering experience, including strong expertise in Google Stack, BigQuery, SQL, and Python. This individual will be responsible for building scalable data pipelines, transforming large datasets, developing analytical models, and delivering actionable insights to support business and technical objectives.
The successful candidate should be comfortable working across the full data lifecycle, from data ingestion and transformation to statistical analysis, data modeling, and reporting. This role requires strong problem-solving skills and the ability to collaborate with data engineers, data scientists, analysts, and business stakeholders.
Design, develop, and maintain scalable data pipelines and data processing workflows using Google Stack.
Build, optimize, and manage complex SQL queries, datasets, tables, and analytical workflows in Google BigQuery.
Develop ETL/ELT pipelines to ingest, transform, validate, and integrate data from multiple sources.
Use Python for data processing, exploratory data analysis, automation, and analytical model development.
Apply statistical methods, data mining, and machine learning techniques to identify patterns, trends, and business insights.
Perform data cleaning, feature engineering, data transformation, and data quality validation.
Design efficient data models and optimize BigQuery performance, query execution, and storage costs.
Develop and maintain reliable data workflows using appropriate Google Cloud data services.
Work with large and complex datasets to support predictive analytics, forecasting, and data-driven decision-making.
Collaborate with data engineering, analytics, product, and business teams to translate requirements into scalable data solutions.
Implement data validation, monitoring, troubleshooting, and performance optimization processes.
Document data pipelines, transformation logic, analytical methodologies, and technical solutions.
Follow best practices for data security, governance, access controls, and data integrity.
Strong experience in Data Science, Data Engineering, or a combination of both.
Hands-on experience with Google Stack and Google BigQuery.
Advanced SQL skills, including complex queries, joins, window functions, aggregations, and query optimization.
Strong Python programming skills for data manipulation, analysis, automation, and data processing.
Experience designing and implementing ETL/ELT pipelines and data integration workflows.
Knowledge of data warehousing concepts, dimensional modeling, and analytical data structures.
Experience working with large datasets and developing scalable data processing solutions.
Understanding of statistical analysis, exploratory data analysis, and data visualization.
Familiarity with data quality checks, pipeline monitoring, and troubleshooting.
Strong analytical, problem-solving, communication, and collaboration skills.
Experience with Google Cloud services such as Cloud Storage, Dataflow, Dataproc, Pub/Sub, and Cloud Composer.
Familiarity with Apache Airflow or other workflow orchestration tools.
Experience with machine learning libraries such as scikit-learn, pandas, NumPy, and TensorFlow.
Knowledge of predictive modeling, feature engineering, and model evaluation techniques.
Experience with dbt or similar data transformation frameworks.
Familiarity with Looker, Looker Studio, Tableau, or Power BI.
Experience with Git, CI/CD, automated testing, and data pipeline deployment practices.
Understanding of data governance, metadata management, and cloud data security best practices.
A hands-on professional who can bridge the gap between data engineering and data science.
Someone who can build reliable data pipelines while also extracting meaningful insights from complex datasets.
Strong ownership of data quality, scalability, performance, and analytical accuracy.
Ability to work effectively in a collaborative, hybrid environment and communicate technical findings to both technical and nontechnical stakeholders.
Location: San Francisco, California (Hybrid)
Duration: Long-term contract with the potential for conversion to full-time employment
Compensation: $90/hour on 1099, all-inclusive
Work Arrangement: Hybrid; candidates must be able to work onsite as required.
