Data Engineer - TS - Washington DC area

Education Requirement:
Bachelor's Degree

• Assist in designing, developing, and maintaining basic ETL pipelines for ingesting, transforming, and loading datasets under the guidance of more experienced engineers.
• Support analysis of data structures, mappings, and data quality checks to identify issues or gaps.
• Help ensure data accuracy, consistency, and integrity by running validation queries, profiling datasets, and supporting data cleanup efforts.
• Contribute to data migration tasks, such as mapping source data to target systems and running migration scripts.
• Collaborate with analysts, data scientists, and business stakeholders to translate requirements into simple data transformations or pipeline updates.
• Monitor pipeline performance and assist in troubleshooting operational issues, escalating complex problems as needed.
• Help document data flows, transformation logic, and operational processes to support maintainability and knowledge sharing.

• 0-1 Years of Professional Experience
• Foundational exposure to data analysis, ETL concepts, or data migration activities-via coursework, internships, personal projects, or early professional experience.
• Working knowledge of SQL, including writing basic queries, joins, and aggregations.
• Familiarity with Python for data manipulation or automation tasks (introductory level acceptable).
• Introductory experience with ETL or workflow tools such as Apache Airflow, Talend, or similar platforms.
• Understanding of basic data warehousing concepts, such as staging, fact/dimension models, or schema structure.
• Exposure to cloud-based data storage or compute platforms (e.g., AWS S3/Redshift, Google Big Query, Azure Storage).

• Hands on or coursework experience with cloud ecosystems such as AWS, Azure, or Google Cloud Platform.
• Exposure to big data technologies (Hadoop, Spark, or distributed processing frameworks).
• Familiarity with data visualization tools (Power BI, Tableau, Looker) and version control systems such as Git.
• Experience building or supporting automated data workflows using orchestration tools or scheduled scripting.

• Foundational ETL development and data pipeline understanding
• Data profiling and validation
• SQL and Python basics
• Understanding of data warehousing fundamentals
• Collaboration with analysts, engineers, and business stakeholders
• Problem solving mindset and willingness to learn
• Clear communication and strong documentation skills
• Adaptability in fast paced, evolving environments