Art of the possible.

Senior Data Engineer

Data EngineerData EngineerFull TimeRemoteTeam 10,001+Since 1954H1B SponsorCompany Site LinkedIn

Location

United States

Posted

29 days ago

Salary

$110.5K - $149.5K / year

Bachelor Degree5 yrs expEnglishAWSCloudETLNumpyOraclePandasPostgresPythonSQLTableau

Job Description

• Provide advanced analytical, machine learning, and data engineering support. • Design, develop, and deploy machine learning models for classification, regression, time series forecasting, and natural language processing applications. • Build and optimize automated, scalable ETL/ELT pipelines using Python, SQL, and cloud-based tools. • Develop and maintain production ML systems including model deployment, monitoring, versioning, and performance tracking. • Design, develop, and deploy interactive dashboards and data visualizations. • Perform end-to-end model development including exploratory data analysis, feature engineering, hyperparameter tuning, model validation, and documentation. • Develop and maintain data pipelines and workflows using tools such as AWS services, Databricks, and GitLab CI/CD. • Conduct data mining, cleaning, and manipulation using SQL, Python (Pandas, NumPy), or R.

Job Requirements

At least 5 years of experience in data science, machine learning, or advanced analytics; 3 years of experience with a master's degree.
Strong proficiency in Python and SQL for data manipulation, analysis, and pipeline development.
Experience with ETL/ELT pipeline development and data engineering best practices.
Demonstrated knowledge of data visualization platforms (Tableau, Power BI) and ability to translate technical insights into executive-level dashboards.
Experience with cloud platforms and modern data infrastructure.
Knowledge of statistical analysis and modeling techniques.
Understanding of relational and non-relational databases (Oracle SQL, PostgreSQL, etc.).
Strong version control and collaboration skills using Git (GitHub, GitLab).
Exceptional analytical skills with strong attention to detail.
Strong written and verbal communication skills with ability to present complex findings to non-technical stakeholders.
Familiarity with ML orchestration tools (e.g. Kubeflow, ML Flow, Air Flow, Sage Maker, or similar) is preferred.
Experience with MLOps practices including model monitoring, versioning, and production deployment is preferred.
Experience working with federal government data systems and compliance requirements is preferred.
Background in Agile/Scrum methodologies and project management tools (Jira) is preferred.
Experience mentoring junior data professionals and establishing analytics best practices is preferred.