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.

The job description provides no details about responsibilities, qualifications, technologies, compensation, location, travel, or work authorization requirements.

Build workflow-specific data pipelines, integrations, and data models supporting AI solutions. Map data across systems, files, emails, and spreadsheets; transform unstructured documents into structured data; establish data quality checks; resolve data issues; and contribute reusable workflow data to the shared AI platform.

Build and maintain scalable SQL and Python ETL/ELT pipelines, Hive/Spark data warehouse workflows, orchestration processes, analytics-ready datasets, data quality checks, monitoring, backfills, and self-service reporting infrastructure. Partner with data scientists, analysts, architects, and operations stakeholders to support workforce, staffing, capacity, productivity, and operational reporting.

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.

Develop and maintain data pipelines, integrations, data models, and enterprise data platforms for the City of Philadelphia. Use Python, SQL, Airflow, databases, cloud tools, and related technologies to connect diverse systems, improve data quality and governance, support open-data initiatives, and document technical solutions. Collaborate with technical and nontechnical stakeholders to enhance public data accessibility and city services.

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.

Designs and delivers full-stack data solutions across collection, ingestion, processing, storage, access, and delivery. Builds scalable pipelines, data models, ETL processes, analytical databases, and reporting solutions. Maintains performance, security, access controls, encryption, backup and recovery, and data quality. Establishes standards and best practices for data objects while troubleshooting production systems and evaluating new technologies.

Build and operate production data pipelines, CDC streams, and Snowflake Bronze-layer infrastructure using Python, SQL, Redpanda, and Dagster. Maintain source adapters, historical snapshots, schema-drift detection, deduplication, data-quality checks, reconciliation, and observability. Collaborate on Silver-layer modeling, governance tagging, CI/CD, containerization, and HVR-to-Redpanda migration. Participate in incident response and develop deeper ownership of orchestration, streaming, data governance, and dbt-based layered modeling.

Design, build, and optimize scalable data pipelines, analytics tools, and cloud-based data solutions. Develop modular production code, conduct peer reviews, apply test-driven development, document systems, and support production data environments. The role involves distributed systems, big data platforms, Kubernetes, cloud migration, stakeholder communication, and technical leadership supporting USTRANSCOM JFOSS.

Build and maintain production data pipelines and ingestion solutions using Python and AWS services. Support data lakes and lakehouse technologies including S3, Glue, Lake Formation, Apache Iceberg, Snowflake, and Databricks. Manage CI/CD pipelines with Jenkins and Bitbucket, troubleshoot pipeline issues, improve data quality, and collaborate with platform and engineering teams on shared data services.

Design, develop, maintain, and optimize scalable data pipelines and cloud-based data solutions. Build relational and dimensional models for OLTP and OLAP systems, collaborate with stakeholders on enterprise reporting models, and administer data platforms for performance, scalability, and reliability. The role supports internal Finance, Manufacturing, Purchasing, and Sales teams using Azure and on-premises data technologies.

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.

Manage and analyze mine-site operational data, consolidating planning, production, maintenance, and performance information into a centralized Technical Data Center. Deploy and support Python, Lua, SQL, and on-premises applications; monitor servers and infrastructure; troubleshoot operational applications; and collaborate with technical, operating, innovation, and business improvement teams on data integration and innovation initiatives.

Designs and implements advanced analytics capabilities by building Azure-based data platforms, ingestion pipelines, and scalable data processing solutions. The role uses Python, Spark SQL, Azure Data Factory, Azure Storage, and Azure Databricks to transform structured and unstructured data. Responsibilities also include querying large datasets, applying Azure networking and security practices, and potentially using Snowflake to create data marts.

Lead data engineering role responsible for administering SQL Server and Amazon Redshift, building Python ETL/ELT pipelines, developing data warehouse models, and preparing data for AI and ML applications. The role includes database performance tuning, AWS data platform security, workflow orchestration, CI/CD, production support, release management, and on-call ownership. Collaboration with product and marketing teams supports modernization of data environments and broader analytics adoption.

Designs, develops, and optimizes scalable data pipelines, dataflows, schemas, and ETL artifacts using AWS and distributed data technologies. Partners with business, technical, and government stakeholders to deliver data solutions, support deployed applications, resolve technical issues, and maintain data governance. The role also contributes to product vision and roadmaps, collaborates with a government product team, documents architectures, and provides occasional off-hours deployment support.

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 scale programmable data infrastructure supporting Vesalius Therapeutics’ human cellular biology platform. The role contributes to large-scale genetics, genomics, stem cell, cellular phenotyping, and artificial intelligence initiatives designed to uncover causal biology and identify therapeutic intervention points.

Build and maintain data pipelines and platform components for analytics, machine learning, and AI workloads. Implement ingestion, transformation, orchestration, testing, validation, deployment, monitoring, and troubleshooting tasks under experienced guidance. Collaborate with consultants and clients, participate in design discussions, code reviews, and agile ceremonies, document work, communicate progress, and develop foundational data engineering expertise.

Develops data acquisition strategies, integrations, migrations, workflows, architectures, and data management procedures. Builds and maintains data pipelines, ensures data quality and security, supports cloud and healthcare claims data environments, and prepares data for analytics, machine learning, and predictive modeling. Collaborates with business and technical stakeholders to define data standards, requirements, monitoring capabilities, and process improvements.

Migrates customer data from legacy and competitor systems into SaaS products using SQL-based tools. Performs extraction, transformation, cleanup, validation, auditing, data mapping, and quality assurance. Develops repeatable scripts and workflows, collaborates with customers and cross-functional teams, supports testing and defect resolution, and documents migration processes. The role increasingly uses BigQuery, cloud platforms, automation, and intelligent tools while supporting global delivery across regions and time zones.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.