Data Engineer 2 - TS required - Washington DC area

Education Requirement: Bachelor’s DegreeKey Responsibilities: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.Required Skills & Experience0-1 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 BigQuery, Azure Storage).Bonus / Preferred QualificationsHands 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.Core Skills & CompetenciesFoundational ETL development and data pipeline understandingData profiling and validationSQL and Python basicsUnderstanding of data warehousing fundamentalsCollaboration with analysts, engineers, and business stakeholdersProblem solving mindset and willingness to learnClear communication and strong documentation skillsAdaptability in fast paced, evolving environments

Education Requirement: Bachelor’s DegreeKey Responsibilities: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.Required Skills & Experience0-1 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 BigQuery, Azure Storage).Bonus / Preferred QualificationsHands 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.Core Skills & CompetenciesFoundational ETL development and data pipeline understandingData profiling and validationSQL and Python basicsUnderstanding of data warehousing fundamentalsCollaboration with analysts, engineers, and business stakeholdersProblem solving mindset and willingness to learnClear communication and strong documentation skillsAdaptability in fast paced, evolving environments