Top Data Engineer Jobs

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

Develop and advance the enterprise data platform by gathering business requirements, designing data models and warehouses, and building data visualization solutions. Administer reporting, analytics, and visualization platforms while promoting best practices. Collaborate cross-functionally, apply business and process knowledge, manage multiple projects, communicate strategically, and provide technical leadership in data engineering and business intelligence.

Build and maintain dbt data models and pipelines, implement real-time ingestion with PySpark and AWS Glue, and apply software engineering practices to analytics development. Collaborate with data scientists, stakeholders, IT teams, and data stewards on data quality, governance, integrations, and deployment. Translate requirements into Jira stories, manage milestones, review Git pull requests, and support enterprise data activation and advanced analytics.

Develop and maintain scalable ELT/ETL pipelines using Snowflake, Dataiku, Databricks, SQL, and Python. Build finance data marts, automate workflows, enable Tableau analytics, and implement data quality, governance, monitoring, testing, and CI/CD practices. Partner with Finance, FP&A, and Accounting stakeholders to translate business requirements into reliable, auditable data products while modernizing manual processes and optimizing performance and cloud costs.

Lead migration of on-premises databases and ETL workloads to Azure and Snowflake. Design scalable data platforms using Azure Data Factory, Databricks, ADLS, Logic Apps, Blob Storage, and Python. Modernize SSIS, T-SQL, and legacy ETL solutions while developing AI-powered capabilities using LLMs, semantic search, vector databases, document intelligence, and RAG architectures. Work independently, collaborate effectively, adopt new technologies, and travel up to 10%.

Develop Python data pipelines and business data objects for applications. Build and deploy CI/CD pipelines, implement source-to-target mappings, resolve complex technical problems, interpret software design requirements, and perform unit and integration testing. The role also promotes software quality practices, cross-functional collaboration, and continuous improvement. It is onsite five days per week in Chicago or Peoria, Illinois.

Develop and maintain scalable data pipelines and secure infrastructure for multimodal synthetic data, AI evaluation, real-time model monitoring, and clinical research data. Responsibilities include data transformation, preprocessing, quality assurance, database management, privacy compliance, interactive dashboards, pipeline documentation, and scientific dissemination.

Designs, builds, administers, and supports cloud data architectures, databases, schemas, metadata repositories, ETL processes, and analytic models. Leads data quality, governance, troubleshooting, testing, automation, reporting, dashboards, and infrastructure design. Collaborates with Agile teams to translate business requirements into technical capabilities and supports decision-making for Audit and business management. The role is full-time onsite at an eligible Federal Reserve location and requires extensive security screening.

Develop and maintain Python data pipelines to produce business data objects supporting applications. Build and deploy CI/CD pipelines, implement source-to-target mapping as pipeline code, and perform unit and integration testing. Solve complex application problems, interpret design requirements, uphold software quality standards, and mentor team members. Work onsite in Chicago or Peoria, IL.

Design, develop, maintain, troubleshoot, and document scalable data pipelines and infrastructure using AWS, Airflow, Snowflake, Git, and Azure DevOps. Collaborate with analytics engineering teams to support One Funnel and OneSite initiatives and enable data-driven business decisions. Apply SQL and Python to solve business challenges, improve system reliability, implement CI/CD and automated testing, and manage data engineering requirements in an Agile environment.

Design, build, and manage scalable batch and real-time data pipelines, databases, and processing systems. Develop and administer Databricks and Snowflake environments, optimize data architecture and distributed processing, manage PostgreSQL and MySQL databases, and implement governance, monitoring, alerting, and troubleshooting practices. Collaborate with analytics, engineering, and product teams to deliver reliable data solutions, document workflows, and promote modern data engineering practices.

Build and operate scalable data pipelines and trustworthy data models supporting Layla’s AI-powered travel products. Own data quality, reliability, observability, experimentation, and model-evaluation foundations. Enable production AI/ML systems through feature and data availability, evaluation datasets, and monitoring. Partner with Product, Analytics, and ML teams to deliver dependable insights, establish engineering best practices, and mentor other engineers.

Develop data products, pipelines, and business intelligence reporting solutions using cloud technologies, large-scale databases, streaming systems, ETL/ELT, and data warehouses. Translate business requirements into technical solutions, analyze high-volume datasets, troubleshoot SQL and pipeline issues, support user acceptance testing, and identify insights, patterns, anomalies, and trends. The six-month co-op includes mentorship, leadership development, and project presentation opportunities.

Design, build, and operate large-scale ETL pipelines, data lakes, and data platforms processing billions of daily events. Own data quality, freshness, reliability, monitoring, and observability for foundational datasets. Develop self-service ETL frameworks and tooling, contribute to architecture and technical strategy, and partner with product, engineering, machine learning, data science, and leadership teams to deliver scalable data solutions.

Designs, develops, and maintains data platforms, pipelines, and integration solutions across on-premises and cloud environments. Responsibilities include resolving data quality and security issues, building ELT processes, developing data scripts and relational or non-relational models, and monitoring data lake performance and scalability. The role also supports analytical and operational decision-making through reliable data solutions and collaborates with cross-functional teams.

Build and operate scalable growth data pipelines, models, feature datasets, and measurement systems supporting paid acquisition, ad bidding, experimentation, attribution, and audience targeting. Partner with Growth, Marketing, Analytics Engineering, and Data Science to deliver reliable data products, productionize machine-learning workflows, maintain data quality and observability, and improve platform performance, cost efficiency, and developer experience.

Architect, deploy, and operate core data platform services (Airflow, Superset, Terraform). Build CI/CD pipelines, automate with Python, manage cloud infrastructure, implement security and compliance, develop dbt data models, catalog assets in OpenMetadata, and mentor junior engineers.

Build and operate production-grade ELT pipelines from Benchling, Salesforce, and third-party systems into Snowflake using dbt. Own ingestion, transformation, warehouse, BI, governance, monitoring, testing, access controls, privacy, cost, and performance. Partner with AI engineering to provide trusted data for internal AI applications and agentic workflows, contribute to warehouse and metrics-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

Build and maintain data pipelines integrating laboratory instruments, automation systems, operational platforms, APIs, robotics, databases, and files. Develop SQL transformations, data models, validation checks, orchestration, monitoring, and alerting to support analytics, dashboards, troubleshooting, and AI systems. Document infrastructure and datasets for regulatory compliance while collaborating with engineering, laboratory operations, data science, and automation teams. This is an on-site role with on-call and occasional weekend or holiday support.

Design, build, optimize, and support scalable batch and real-time data pipelines and enterprise data products. Develop reusable frameworks, ingestion automation, testing, CI/CD, and data-quality solutions while ensuring governance, security, lineage, observability, and policy-compliant data segregation. Troubleshoot production issues, improve performance and cost efficiency, and collaborate with engineers, data stewards, scientists, product managers, and business stakeholders across global teams.

Design, build, test, and maintain secure, scalable data pipelines and architectures. Work with Python, Databricks, SQL/NoSQL, and enterprise AI capabilities to deliver trusted data collection, storage, access, and analytics. Validate AI-assisted outputs, update data models, customize tools, and ensure controls, traceability, and data sensitivity handling.

Leads enterprise data engineering architecture and SAP data conversion initiatives. Designs scalable ingestion, transformation, storage, and delivery patterns; builds BODS ETL workflows; manages extraction, cleansing, mapping, validation, reconciliation, and loading. Establishes automation, governance, quality, lineage, security, observability, and infrastructure-as-code practices. Coordinates analysts, SAP teams, engineers, business owners, vendors, and consultants through mock conversions, cutovers, rollback, and production operations while coaching teams and resolving critical issues.

Build and maintain analytics data models, warehouse tables, views, and automated pipelines using dbt and Snowflake. Ensure data quality, consolidate business-unit data, develop ThoughtSpot dashboards, and analyze complex datasets to support product and operational decisions. Partner with product, engineering, and operations stakeholders, define metrics, improve self-service analytics, leverage AI tools for automation, and document data processes.

Build and maintain data pipelines, ingestion workflows, transformations, and data products using Python, SQL, and cloud data platforms. Test and troubleshoot systems while applying Git, software testing, and CI/CD practices. Collaborate with senior engineers and cross-functional stakeholders, use AI-assisted tools, and deliver reliable data solutions in a fast-paced startup environment.

Provide technical leadership for analytics engineering across enterprise platforms. Design and optimize scalable Databricks data pipelines using Python, PySpark, and SQL; develop governed Power BI solutions, semantic models, dashboards, and executive reporting. Partner with stakeholders to define metrics, deliver AI-enabled insights, and improve decision-making. Ensure data quality, lineage, security, and governance while optimizing models, queries, and pipelines. Lead large-scale technical initiatives, influence architecture, and guide analytics engineers.