Note: The job is a remote job and is open to candidates in USA. Mindlance is seeking an IT Data Engineer to support cloud-based data engineering initiatives. The role focuses on building reliable AWS data pipelines, scalable data structures, automated ingestion and transformation workflows, curated datasets, and data models that enable analytics, reporting, dashboards, and GenAI-enabled applications. The contractor will collaborate with data scientists, analysts, business stakeholders, and technical platform teams while supporting data quality, monitoring, metadata management, and documentation.
Responsibilities
- Design, build, and maintain cloud-based data pipelines for structured and semi-structured/unstructured data sources
- Develop ingestion, transformation, and refresh workflows using tools such as AWS S3, Glue, Athena, Lambda, Step Functions, DynamoDB, relational databases, and Python-based automation
- Create curated datasets, metadata tables, and reusable schemas that support analytics, dashboards, reporting, and application development
- Build data models that link related business objects using reliable identifiers, keys, and reference tables
- Develop SQL queries, views, and data access layers for recurring analytical and reporting needs
- Partner with data scientists and analysts to prepare clean, trusted datasets for downstream modeling, GenAI workflows, dashboards, and prototype applications
- Implement data quality checks, validation rules, exception handling, logging, and pipeline monitoring
- Document data sources, transformations, assumptions, refresh logic, and known limitations
- Support migration from manual or file-based workflows to automated, scalable cloud data pipelines
- Collaborate with platform, security, and infrastructure teams to follow enterprise standards for access, data handling, and operational reliability
Skills
- 5–8 years of relevant experience in data engineering, analytics engineering, cloud data platforms, ETL/ELT development, database design, or data integration
- 5+ years: Able to independently build reliable data pipelines and queryable datasets
- 7–8 years: Able to define data architecture patterns, design reusable data models, improve operational reliability, and help scale prototype pipelines into more durable data products
- Bachelor's degree in Computer Science, Data Engineering, Information Systems, Software Engineering, Engineering, Applied Mathematics, or a related technical field
- Strong hands-on experience with cloud data engineering, SQL, Python, metadata management, and modern data lake patterns
- Comfortable working in AWS-based environments
- Ability to design pipelines that move data from raw sources into curated, queryable, and application-ready layers
- Ability to support data quality checks, logging, monitoring, repeatable refresh processes, and clear schema/documentation practices
- Design, build, and maintain cloud-based data pipelines for structured and semi-structured/unstructured data sources
- Develop ingestion, transformation, and refresh workflows using tools such as AWS S3, Glue, Athena, Lambda, Step Functions, DynamoDB, relational databases, and Python-based automation
- Create curated datasets, metadata tables, and reusable schemas that support analytics, dashboards, reporting, and application development
- Build data models that link related business objects using reliable identifiers, keys, and reference tables
- Develop SQL queries, views, and data access layers for recurring analytical and reporting needs
- Partner with data scientists and analysts to prepare clean, trusted datasets for downstream modeling, GenAI workflows, dashboards, and prototype applications
- Implement data quality checks, validation rules, exception handling, logging, and pipeline monitoring
- Document data sources, transformations, assumptions, refresh logic, and known limitations
- Support migration from manual or file-based workflows to automated, scalable cloud data pipelines
- Collaborate with platform, security, and infrastructure teams to follow enterprise standards for access, data handling, and operational reliability
- Master's degree or equivalent experience in data engineering, cloud architecture, analytics engineering, or enterprise data platforms
Benefits
- Remote work arrangement
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