Top Entry Level Data Engineer Jobs

Build and maintain scalable data pipelines, analytical data models, backend data systems, business rules, metrics, and data sources. Partner with teams across finance, growth, product, customer support, sales, and marketing to support analytical needs and improve data-driven decision-making. Own data engineering problems end to end and help advance organizational data practices.

Build and operate governed Finance data infrastructure on Databricks, including scalable pipelines, integrations, semantic layers, data-quality controls, observability, and AI-enabled services. Partner with analytics and enterprise teams on architecture, data contracts, security, recoverability, and production ownership. Optimize platform costs through FinOps practices and use AI coding assistants while validating performance, correctness, and security.

Build and maintain Databricks data pipelines using SparkSQL and PySpark; ingest, transform, model, and validate healthcare data from payer, provider, platform, and API sources. Support automated file exchanges, dashboards, downstream workflows, testing, governance, access controls, and audit logging. Participate in design and code reviews, use AI coding tools responsibly, and progressively own data engineering work while handling protected health information.

Build and maintain Databricks data pipelines using SparkSQL and PySpark, integrating payer, provider, claims, platform, and API data. Develop transformations, models, file exchanges, dashboards, automated workflows, tests, and data-quality validations. Collaborate in design and code reviews, use AI coding tools responsibly, and protect PHI through governance, access controls, and audit logging.

Designs, develops, and maintains scalable data pipelines, transformations, datasets, and curated data products for credit risk, reporting, analytics, decisioning, and machine learning. The role uses SQL, Python, Databricks, and Spark; implements data quality controls; supports production monitoring and troubleshooting; collaborates with analytics, data science, product, and engineering teams; and contributes to cloud migration and data platform modernization.

Develop and maintain scalable data pipelines, integrations, data lake structures, and analytical solutions for operational risk intelligence. Analyze incidents and risk events, identify trends and control gaps, and produce dashboards, reporting, and executive-ready insights. Troubleshoot data and workflow issues, improve automation and data quality, support governance and security standards, and collaborate with engineering teams and stakeholders on operational intelligence improvements.

Build and support scalable data pipelines, ETL/ELT processes, orchestration workflows, APIs, and cloud data services on Google Cloud Platform. Integrate enterprise platforms with governance systems for metadata discovery, lineage, classification, and policy enforcement. Develop secure, observable, cost-efficient solutions using CI/CD, testing, automation, DataOps, and DevSecOps practices, while contributing to AI-enabled workflows and reusable platform frameworks.

Build and maintain scalable data infrastructure and ETL/ELT pipelines using Python, SQL, NiFi, Linux, and database technologies. Integrate structured and unstructured data, ensure data quality and security, optimize systems, and document reproducible solutions. Collaborate with stakeholders, deliver technical briefings, use machine learning and NLP techniques, and explain complex concepts to junior data scientists. The role is full-time, onsite in McLean, Virginia, and requires an active TS/SCI clearance with CI polygraph.

Own and evolve the club’s cloud-based data platform, building scalable pipelines for performance analytics, reporting, recruitment, opposition analysis, and sports science. Perform database administration, including tuning, indexing, backups, and access management. Develop AI agents for pipeline monitoring, maintain code quality through Azure DevOps, and ensure data governance, privacy, quality, documentation, and platform support while collaborating with data scientists, coaches, and sports science teams.

Develop and optimize SQL Server and Snowflake data solutions, migrate workloads to the cloud, and build ETL/ELT pipelines using SQL, Python, Airflow, and AWS. Responsibilities include performance tuning, data validation, reconciliation, monitoring, production troubleshooting, and support for transaction cost analysis products using large-scale financial, trade, and market data.

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.

Leads data engineering for a functional area by defining future-state architecture, establishing technical standards, mentoring team members, developing code and procedures, fixing bugs, maintaining quality, gathering requirements, and supporting application functionality. The role serves as a subject matter expert, partners with architecture and application engineering leaders, and drives technology adoption and professional development.

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.

Builds and maintains data pipelines and datasets supporting analytics, AI, and modeling. Responsibilities include ingesting healthcare claims and lab data into Snowflake and AWS Iceberg, processing structured and unstructured sources, curating data for AI use cases, implementing medallion architectures, evaluating data quality, and contributing to pilot initiatives using SQL, Python, AWS Glue, Terraform, and Snowflake.

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.

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.

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.

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.

Design, deploy, and maintain scalable data pipelines, ETL/ELT workflows, data models, warehouses, and analytical platforms. Integrate databases, APIs, SaaS systems, telemetry, and operational data while establishing data quality, monitoring, security, and access controls. Optimize performance, scalability, and costs, and collaborate cross-functionally to support reporting, anomaly detection, predictive insights, AI recommendations, and customer-facing products.

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.