Top Entry Level Data Engineer Jobs

Build and scale distributed data pipelines and architectures supporting AI products and research. Responsibilities include processing large datasets with Spark, designing AWS data systems, managing SQL and NoSQL storage, writing Python and SQL, ensuring data quality and reliability, and implementing automation, orchestration, monitoring, and scalable data operations.

Develop and maintain scalable data pipelines, ETL/ELT workflows, and data applications for reporting and analytics. Retrieve, parse, and process structured and unstructured data while designing and maintaining analytic solutions. Collaborate with analysts, developers, engineers, and data consumers in an agile environment. The role supports mission-driven client projects and requires eligibility for a Secret clearance.

Designs, integrates, and manages scalable batch and streaming data pipelines from internal and external sources. Responsibilities include data transformation, schema and business-rule enhancements, load monitoring, troubleshooting data availability, evaluating emerging technologies, and collaborating with business, engineering, and privacy teams. The role also supports data governance, secure data handling, cloud technologies, and adoption of AI-enabled tools.

Designs, integrates, and manages scalable batch and streaming data pipelines from internal and external sources. Builds ETL processes, data schemas, business rules, and cloud-based data solutions; monitors data loads and resolves availability issues. Collaborates with business, engineering, and privacy teams to ensure compliant data handling. Evaluates emerging data technologies, supports innovation, and helps adopt modern architectures, DevOps practices, and AI-enabled workflows.

Design, develop, test, and implement data engineering features using Python and PySpark. Build standalone scripts, perform complex data transformations, translate business requirements into technical solutions, troubleshoot data and software issues, and enhance existing frameworks. Apply object-oriented programming, coding best practices, automated testing, documentation, and large-scale data processing while collaborating with team members and working independently.

Designs and maintains scalable data infrastructure, including ETL/ELT pipelines, data lakes, warehouses, validation processes, and governance controls. Collaborates with architects and business stakeholders to translate requirements into reliable data solutions. Uses Docker, Kubernetes, cloud platforms, Spark, and orchestration tools to deploy and automate data services. Supports AI and automation initiatives involving LLMs and agents while improving data quality, security, and accessibility.

Build and maintain Nexus, an enterprise AI implementation platform. Develop backend services, APIs, integrations, orchestration workflows, automated data pipelines, transformations, semantic models, validation processes, and governed lakehouse assets. Partner with AI engineers and enterprise customers to deploy solutions, resolve complex data and system requirements, and create reusable integrations. Ensure secure, monitored, recoverable, and repeatable deployments across customer environments.

Design and implement enterprise data platforms, lakehouse architectures, ingestion pipelines, knowledge indexes, and retrieval services for AI applications. Define data contracts, schemas, APIs, governance, permissions, lineage, and quality controls across structured and unstructured sources. Operate reliable data and retrieval services, troubleshoot failures, and assess relevance, latency, scalability, and freshness while partnering with AI Engineering. U.S. citizenship and eligibility for Public Trust or Suitability/Fitness determination are required.

Design and implement enterprise data platforms, pipelines, connectors, knowledge indexes, and retrieval services for AI applications. Integrate structured and unstructured data while maintaining permissions, lineage, quality, freshness, and reliability. Define data contracts, schemas, APIs, and governance patterns; troubleshoot ingestion and retrieval issues; and partner with AI engineers to improve retrieval behavior and response quality.

Develop scalable, maintainable, and high-performing data pipelines and enterprise data lake solutions. Partner with stakeholders and technology teams to create analytics solutions, reports, and dashboards that improve patient outcomes, operational efficiency, and data-driven decision-making. Support data quality and enterprise data initiatives across the organization.

Entry-level rotational Data Engineer responsible for SQL development, SSIS data transformations, data validation, testing, performance tuning, data profiling, and reporting solutions. The role collaborates with IT, project managers, analysts, and business stakeholders to implement enterprise data solutions across Salesforce, Marketing Cloud, and other platforms. This is a 100% on-site position in San Diego, California.

Build and maintain production-grade data pipelines, connectors, and harmonized snapshots across clinical studies and applications. The role covers ingestion, transformation, orchestration, data modeling, quality controls, monitoring, lineage, auditability, regulatory compliance, CI/CD, and integration with Databricks and Snowflake environments. The engineer will validate source data, manage schema changes, optimize performance, and collaborate with platform and AI engineers to support reliable data and AI applications.

Build and deploy scalable data pipelines and platforms that organize structured and unstructured data for mission-driven client projects. Collaborate with analysts, developers, data engineers, and data consumers in an agile environment. Support data assessment, exploration, examination, platform design, development, and maintenance. The role requires Palantir Foundry, Python, CI/CD, SQL, TS/SCI clearance, and travel to OCONUS locations up to 25% of the time.

The Associate Data Engineer role is listed with KKR, but the provided description contains only company information and equal-opportunity notices. No specific responsibilities, technologies, qualifications, salary, travel, or clearance requirements are provided.

Build and maintain data engineering solutions using Python, PySpark, and big data technologies. Work with AWS services including S3, EMR, and Lambda, while supporting infrastructure through Terraform. Integrate Java, REST APIs, messaging systems, Kafka, Cassandra, Airflow, Control-M, and native schedulers. Databricks experience and familiarity with machine learning workflows are preferred.

Designs and deploys scalable data pipelines and ETL/ELT processes using Azure and Databricks. Develops Python, PySpark, and SQL solutions, builds relational and analytical data models, optimizes pipeline performance, and ensures data quality. Serves as a technical SME for vendor developers through code reviews, quality standards, delivery oversight, and collaboration with product owners and business stakeholders.

Executes scoped data engineering and software development tasks, supports client issue triage, onboarding, offboarding, monitoring, break-fix work, testing, documentation, and operational maintenance. Collaborates with senior engineers and cross-functional teams while using version control and AI-assisted development tools. The role is designed for an early-career professional building engineering fundamentals and gaining experience with production client systems and data pipelines.

Performs ETL data migration from legacy utility billing systems into SpryPoint’s platform. Develops Python and SQL scripts, profiles source databases, maps and transforms datasets, validates data quality, and supports reliable go-live integrations. Collaborates with technical and client teams, documents best practices, improves automation and observability, and participates in iterative reviews. This early-career role emphasizes data conversion, auditing, process optimization, and continuous learning.

Leads the migration of legacy data warehouses and pipelines to an Azure and Databricks lakehouse. Defines technical architecture, reusable Azure Data Factory and Databricks pipelines, reconciliation methods, quality gates, and performance solutions. Mentors data engineers, reviews technical work, and collaborates with product, QA, platform, and customers in a remote Agile environment.

Build and maintain scalable data pipelines, ETL workflows, databases, and cloud-based data storage for digital marketing data. Ensure data quality, accuracy, and performance while preparing datasets for dashboards and reporting. Collaborate with analysts, CSMs, leadership, and cross-functional teams to address data needs, troubleshoot issues, participate in code reviews, and support scalable data management processes.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Build a canonical entity and knowledge graph for identity resolution, permissions, compliance, and agent authorization. Develop production data pipelines across internal systems, graph-based correlation, ML matching, schemas, APIs, provenance, bitemporal models, drift detection, and delegation credentials. Partner with Finance and Compliance on classification pilots while enforcing privacy, regulatory compliance, jurisdictional partitioning, and auditability.

Supports data pipeline development, data modeling, wrangling, semantic layers, dashboards, and reporting using SQL, Python, AWS, and Snowflake. Collaborates with cross-functional teams to translate business requirements into data solutions and applies data governance, security, and quality practices. This co-op is designed for a currently enrolled university student available January 11 through June 25, 2027, and willing to relocate to Framingham, Massachusetts.

Supports the development and maintenance of SQL and Python data pipelines, data models, semantic layers, dashboards, and reports. Performs data wrangling and preprocessing, collaborates with cross-functional teams to translate business needs into data solutions, and applies data governance, security, and quality practices. The role provides hands-on data analytics engineering experience supporting business analytics and AI initiatives.

Supports the development and maintenance of SQL and Python data pipelines, data models, semantic layers, dashboards, and reports. Performs data wrangling and preprocessing, translates business requirements into data solutions, and contributes to data governance, security, and quality assurance. Collaborates with cross-functional teams supporting marketing, engineering, product development, supply chain, finance, business analytics, and AI initiatives.