Data Engineer Cloud Data Pipelines, Integration & Analytics Enablement

The role will help connect data from multiple internal sources, automate data ingestion and transformation, create reusable data models, and enable downstream analytics, dashboards, reporting, and GenAI-enabled applications. "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.

Description: The contractor will support data engineering initiatives focused on building reliable data pipelines, scalable data structures, cloud-based data integration, and analytics-ready datasets. The role will help connect data from multiple internal sources, automate data ingestion and transformation, create reusable data models, and enable downstream analytics, dashboards, reporting, and GenAI-enabled applications.
The contractor will work closely with data scientists, analysts, business stakeholders, and technical platform teams to ensure that data is accessible, well-structured, documented, and usable for decision-making. The role requires strong hands-on experience with cloud data engineering, SQL, Python, metadata management, and modern data lake patterns.
A strong candidate should be comfortable working in AWS-based environments and should be able to design pipelines that move data from raw sources into curated, queryable, and application-ready layers. The contractor should also be able to support data quality checks, logging, monitoring, repeatable refresh processes, and clear schema/documentation practices.
Key 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.
Top 3 Must-Have Skill Sets
1. AWS Cloud Data Engineering
Hands-on experience building data pipelines and data lake workflows using AWS S3, Glue, Athena, Lambda, Step Functions, DynamoDB, RDS or equivalent services. The candidate should understand raw, curated, and consumption-layer data patterns.
2. Pyspark , Python / SQL ETL and Data Automation
Strong Python and SQL skills for extracting, cleaning, transforming, validating, and loading data. Experience working with CSV, Excel, JSON, APIs, databases, file shares, and semi-structured business/technical data is important.
3. Data Modeling, Metadata Management & Integration
Ability to design practical schemas, reference tables, metadata structures, and relational linkages across multiple business processes or systems. The candidate should be able to create durable data models that support analytics, reporting, dashboards, and application backends.
Years of Experience Required
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.
Education Requirements
"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.

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