Top Data Engineer Jobs

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Build and maintain ELT pipelines and dbt models into Snowflake, orchestrate and monitor data workflows on AWS, integrate applications/APIs, perform analysis and create Power BI dashboards, and apply LLM/MCP-based AI tooling to automate and enhance data products while collaborating with senior engineers and stakeholders.

Design, build, and maintain cloud data warehouse infrastructure and ETL pipelines on AWS. Create reports/dashboards, collaborate with analysts and stakeholders, model data for analytics, ensure data quality, automate processes, evaluate new technologies, and mentor junior engineers.

Design, build, and maintain large-scale cloud data platforms, pipelines, data warehouses and lakes. Ensure secure, high-performance data ingestion, transformation, and access. Lead/coach a team, collaborate with data scientists, and influence architecture and governance across the business.

Senior Addepar data engineer/analyst responsible for supporting and scaling Addepar integrations, building and maintaining data pipelines (Addepar↔Snowflake/Canoe/other platforms), diagnosing and resolving data quality issues, developing SQL-based reconciliations and automation, partnering with business and technology stakeholders, and supporting production operations, governance, testing, and documentation for wealth and asset management data.

Build and maintain scalable data pipelines, trusted datasets, and reporting systems to support infrastructure deployment, hardware operations, capacity planning, and supply chain analytics. Partner with stakeholders, improve data quality, lineage, and observability, and enable executive reporting, forecasting, and operational reviews. Integrate new telemetry sources and document systems for long-term maintainability.

Design, build, and maintain scalable data pipelines and data warehouses integrating EMR, revenue cycle, payer, and operational systems. Ensure data quality, governance, HIPAA compliance, and mapping of clinical/financial codes (CPT, ICD-10, HCPCS). Enable dashboards, reporting, and analytics; collaborate with clinical, finance, and IT stakeholders to support auditing, reimbursement analysis, and operational decision-making.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.

Builds and optimizes ETL/ELT pipelines, integrates disparate data sources, ensures data quality/security, supports analysts and data scientists, and implements data governance and metadata management.

Build and maintain scalable data pipelines and architectures, perform ETL into data warehouses/lakes, enforce data governance and quality, manage databases, write optimized SQL, maintain metadata/catalogs, and produce Power BI reports using advanced DAX and Power Query.

Lead design, build, and operationalization of a scalable enterprise data platform and pipelines (batch and streaming) to support analytics and AI. Drive architecture for lakehouse, ETL/ELT, CDC ingestion, observability, governance, and mentor engineers while collaborating with cloud, MDM, and product teams.

Own and maintain end-to-end data infrastructure: build ETL pipelines, write SQL transformations, monitor data quality and anomalies, provision client cloud projects, debug pipeline issues, and respond to ad hoc data requests to keep reporting reliable.

Support DaaS/Digital Marketplace deployments and data set publishing across Google Marketplace, AWS, and Snowflake. Automate uploads/refreshes, QC steps, and product configurations; troubleshoot integrations; create buckets/views/shares; develop repeatable automation frameworks; collaborate in an Agile team and manage multiple tasks independently to ensure timely, accurate delivery of data pipelines and product data sets.

Design, build, and maintain Databricks-based ETL/ELT pipelines and medallion (bronze/silver/gold) lakehouse architectures. Ensure data quality, lineage, governance, and performance for large federal health datasets. Collaborate with data scientists, analysts, and CMS stakeholders and support Agile development and CI/CD workflows.

Lead design, build, and productionization of scalable, secure Azure Databricks data pipelines and data products. Transform raw sources into trusted datasets, enable AI/ML feature-ready data, collaborate with data scientists and stakeholders, drive process improvements, mentor engineers, and solve complex data engineering problems with ownership and technical leadership.

As a Lead Data Engineer, you will design and develop solutions, collaborate with teams on data technologies, and support cloud-based data warehousing and big data applications.

The Lead Data Engineer will design and develop technical solutions, collaborate with agile teams, and implement cloud-based data solutions, focusing on real-time data applications and big data technologies.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

As a Quantitative Developer, you'll innovate and enhance quantitative methods, develop custom tools, maintain data pipelines, and collaborate with researchers for advancing investment strategies.

Design and maintain scalable cloud analytics data models and semantic layers, integrate ERP/project/operational systems, preserve business context, define modeling standards, deliver dashboards and reports, partner with stakeholders on metrics, and support planning and forecasting to enable enterprise reporting and decision-making.

Designs and builds scalable distributed data processing systems and data warehouse solutions on-prem and in AWS. Develops ETL/ELT pipelines, optimizes databases, implements CI/CD, mentors team members, liaises with business units, and ensures performance, reliability, security, and documentation for BI and analytics applications.

Lead design, implementation, automation, and maintenance of large-scale ETL pipelines and scalable data solutions. Optimize SQL and database performance, collaborate with analytics and business stakeholders to translate needs into data requirements, apply best practices (version control, code reviews, data validation), mentor junior engineers, and ensure delivery of accurate, robust data products while complying with internal policies and regulations.

Design, build, and maintain end-to-end data pipelines and integrations using Azure Data Factory, DBT, and Snowflake. Optimize SQL and Snowflake performance, support batch ingestion and API-based integrations, mentor engineers, produce technical documentation and runbooks, and ensure data quality, monitoring, and secure compliant data transfers.

Design, develop, and deploy AI-powered data solutions, optimize ETL processes, build data pipelines, and create executive dashboards, while collaborating with product leaders.

Own and operate the iMData platform: design and build Databricks-based ETL pipelines using PySpark and medallion architecture, create gold-layer star schemas, migrate assets from Synapse to Databricks, implement CI/CD and Terraform IaC, ensure data security and observability, mentor junior engineers, and collaborate with stakeholders and ML engineers to deliver production-grade analytics and AI-ready data.