Data Engineer

We are seeking an experienced Data Engineer to support cloud data engineering initiatives focused on building reliable data pipelines, scalable data structures, and analytics-ready datasets. In this role, you will design and develop data pipelines that bring together information from multiple internal and external sources, automate data ingestion and transformation, and create curated datasets that support analytics, dashboards, reporting, data science, and GenAI-enabled applications. You will work closely with data scientists, analysts, business stakeholders, and technical platform teams to ensure data is accessible, well-structured, documented, and reliable for decision-making and application development. The ideal candidate has strong hands-on experience with AWS data services, Python, SQL, PySpark, ETL/ELT, data modeling, and modern data lake architectures. Data Engineer – AWS / Python / SQL / PySpark Location: 100% Remote Duration: Contract through the end of the year, with potential for extension. NOTE: This role is open to W2 candidates only. We are unable to consider C2C or third-party submissions at this time. Key Responsibilities • Design, build, and maintain cloud-based data pipelines for structured and semi-structured/unstructured data. • Develop data ingestion, transformation, and refresh workflows using AWS services such as S3, Glue, Athena, Lambda, Step Functions, DynamoDB, and RDS. • Build automated ETL/ELT processes using Python, PySpark, and SQL. • Create curated, queryable datasets, metadata tables, schemas, and reusable data structures. • Develop data models that connect related business objects using reliable identifiers, keys, and reference tables. • Develop SQL queries, views, and data access layers to support recurring analytics and reporting requirements. • Partner with data scientists and analysts to prepare clean, trusted datasets for machine learning, GenAI workflows, dashboards, reporting, and prototype applications. • Implement data quality checks, validation rules, exception handling, logging, monitoring, and repeatable pipeline refresh processes. • Document data sources, transformations, business assumptions, refresh logic, schemas, and known data limitations. • Modernize manual or file-based processes by migrating them to automated and scalable cloud data pipelines. • Collaborate with platform, security, and infrastructure teams to follow enterprise standards for data access, security, governance, and operational reliability. Required Qualifications • 5–8 years of professional experience in data engineering, analytics engineering, cloud data platforms, ETL/ELT development, database design, or data integration. • Strong hands-on experience with AWS cloud data engineering and data lake architectures. • Experience with AWS S3, Glue, Athena, Lambda, Step Functions, DynamoDB, RDS, or equivalent AWS services. • Strong programming experience with Python and SQL. • Hands-on experience with PySpark and distributed data processing. • Experience building ETL/ELT pipelines for structured and semi-structured data. • Experience working with data from sources such as CSV, Excel, JSON, APIs, databases, file shares, and other business/technical systems. • Strong understanding of data modeling, schemas, metadata, reference tables, keys, and relational data structures. • Experience implementing data quality, validation, monitoring, logging, and exception-handling processes. • Ability to work effectively with data scientists, analysts, business stakeholders, and technical teams. • Strong documentation and communication skills. Education: Required: Bachelor's degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Engineering, Applied Mathematics, or a related technical field. Preferred: Master's degree or equivalent experience in data engineering, cloud architecture, analytics engineering, or enterprise data platforms.