Senior Data Analytics Engineer
New
P
PropelusHealthcare Technology
Remote - USFull-TimeSenior
Salary100,000 - 140,000 USD per year OTE
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Job Details
- Experience
- 4+ years
- Required Skills
- AWSPostgreSQLPythonSQLApache AirflowGCPGitJavaMySQLSnowflakeAzureData modelingBigQuerydbtRedshift
Requirements
- Bachelor's degree in Computer Science, Data Science, Statistics, or a related field.
- 4+ years of experience in data engineering, analytics engineering, or a similar role.
- Proficiency in SQL and experience with relational databases (e.g., PostgreSQL, MySQL).
- Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and related data warehousing services (e.g., Snowflake, Redshift, BigQuery).
- Familiarity with data testing frameworks.
- Experience with ETL tools and techniques (e.g., Apache Airflow, dbt).
- Solid understanding of data modeling principles and data warehouse architecture.
- Strong analytical and problem-solving skills.
- Experience with at least one programming language (e.g., Python, Java).
- Good communication and collaboration skills.
- Proficiency in using Git for version control and collaboration on codebase.
Responsibilities
- Develop and maintain robust and scalable data pipelines to extract, transform, and load (ETL) data from various sources into our data warehouse.
- Design, build, and optimize data models to support analytical needs and reporting requirements.
- Collaborate with data scientists to implement and deploy machine learning models and analytical solutions.
- Monitor data quality and identify and resolve data integrity issues.
- Contribute to the development of data documentation and metadata management practices.
- Work with stakeholders to understand their data needs and translate them into technical requirements.
- Assist in the evaluation and implementation of new data technologies and tools.
- Participate in code reviews and contribute to the development of coding standards and best practices.
- Independently manage assigned tasks and projects, ensuring timely delivery and quality results.
- Proactively identify opportunities to improve data processes and systems.
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