Top Senior Data Engineer Jobs in Kansas City, MO

Own the organization’s data platform, including production pipelines, cloud warehouse architecture, dimensional data models, data quality, testing, documentation, and lineage. Partner with Finance, HR, and Operations stakeholders to build reliable reporting solutions. The role requires independent technical ownership, performance and cost optimization, schema management, and scalable integration of new data sources and business systems.

Design and operate production-grade data pipelines, backend services, APIs, and agentic AI workflows. Build scalable systems that ingest, transform, and operationalize data for customer-facing and internal applications. Translate ambiguous operational workflows into reliable architectures, improve service observability and performance, and own technical decisions across data modeling, system design, security, and compliance.

Own and expand Chicory’s analytical data platform supporting advertising, campaign, financial, product, and publisher reporting. Design BigQuery architecture, build batch and streaming pipelines with Python, SQL, Beam, Dataflow, and Airflow, and develop governed data marts and BI datasets. Establish data quality, observability, reconciliation, access controls, cost management, and engineering practices. Partner with engineering, finance, product, campaign management, sales, and leadership while leading incident recovery and documenting systems.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Lead technical design and hands-on development of Pantheon’s data platform, extending ingestion, curation, retention services and ML/LLM infrastructure. Mentor engineers, drive architecture and engineering standards, operate in a DevOps model, partner cross-functionally, and participate in on-call support to ensure reliable, compliant data access and analytics.

Provides hands-on technical leadership for foundational data engineering and analytics engineering. Owns multi-year data architecture, production pipelines, dimensional models, and trusted metrics spanning cloud costs, traffic, infrastructure, and AI usage. Designs new datasets integrating cost, utilization, performance, and reliability signals; establishes standards, coaches engineers, aligns stakeholders, and drives adoption across Infrastructure, Finance, Data Science, and Product Engineering.

Own and evolve production data systems supporting healthcare, claims, billing, analytics, and reporting. Design resilient ingestion patterns, data contracts, models, schemas, and pipelines that handle incomplete, duplicated, delayed, and changing data. Improve performance, observability, failure handling, retries, backfills, and replay safety. Collaborate with engineering, product, operations, and analytics teams while making architecture decisions and documenting technical changes.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Leads the architecture, development, and support of enterprise data warehouses, lakehouses, dimensional models, ETL/ELT pipelines, and governed data products using Microsoft Fabric and related tools. Ensures data quality, security, scalability, performance, and reliability for analytics and reporting. Partners with technical and business stakeholders, documents data solutions, guides architecture decisions, mentors team members, and drives continuous improvement of data engineering standards and practices.

Designs and maintains scalable ETL pipelines, data models, integrations, cloud databases, and healthcare data infrastructure. Ensures security, HIPAA/HITRUST compliance, reliability, monitoring, and documentation while collaborating with product, engineering, and clinical teams. Requires strong SQL and Python expertise, modern data warehousing and orchestration experience, cloud platform knowledge, and familiarity with healthcare interoperability and claims data.

Lead Data Engineer responsible for designing, maintaining, and improving data pipelines and platform infrastructure across ingestion, streaming, transformation, and delivery. The role uses Airflow, Spark, Iceberg, Kafka, Flink, dbt, Terraform, and GCP. Responsibilities also include defining data contracts, documenting systems, optimizing performance, reviewing code, supporting access controls and compliance, and collaborating with stakeholders and teammates.

Leads data engineering teams in designing, developing, and supporting scalable batch and streaming data solutions. Responsibilities include data pipeline development, architecture, governance, security, platform optimization, production support, technical mentorship, stakeholder collaboration, and delivery leadership. The role requires hands-on engineering with Python or similar languages, modern data technologies, cloud platforms, DevOps practices, and people-management responsibilities.

Designs and maintains ELT pipelines, warehouse integrations, data models, and data quality processes. Collaborates with data scientists, analysts, engineers, and stakeholders to deliver scalable analytical solutions. Responsibilities include data integration, governance, security, performance optimization, exploratory analysis, visualization, and cloud-based API ingestion. Requires strong SQL, DBT, Snowflake, Azure Data Factory, cloud, and scripting experience, plus leadership and communication skills.

Develops scalable data and software systems using Python and Apache Spark. Designs APIs, data models, ETL workflows, orchestration, and data interactions while optimizing performance, scalability, integrity, and security. Builds user interfaces, contributes to automated testing, applies Agile practices, and supports CI/CD delivery. The role requires strong software engineering, distributed computing, analytical, and communication skills, with AWS, R, Databricks, and SAS experience preferred.

Lead production of federal species range maps for marine and anadromous taxa. Aggregate, normalize, transform, and validate spatial data; analyze habitat intersects; coordinate iterative reviews with FWS and NOAA SMEs; prepare QA documentation; and secure government sign-off for map deliverables.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, cloud data infrastructure, and complex data models. Supports analytics partners, develops data warehouse solutions, tunes SQL and database performance, troubleshoots production issues, and delivers end-to-end data engineering projects. The role collaborates with architects, analysts, developers, and business stakeholders, applies big data technologies, supports agile delivery, and mentors team members.

Design and build a durable data storage platform that preserves source disagreements, provenance, uncertainty, historical versions, and evidence. Develop result and evidence stores, lineage tracking, versioned rules and configurations, reproducible decision records, and point-in-time entity views. Collaborate with entity-resolution and machine-learning teams to support probabilistic outputs. The role requires production data engineering experience, strong Python and SQL, PostgreSQL expertise, U.S. citizenship, and an active Secret clearance.

Designs and implements scalable software solutions, develops maintainable features, resolves bugs, optimizes performance, and writes automated tests. Participates in architecture discussions, code reviews, Agile planning, CI/CD, observability, and adoption of AI-assisted engineering tools. Collaborates with QA, Product Management, and DevOps to deliver software across web, desktop, cloud, and IoT environments. The role is remote for candidates in the Seattle or Los Angeles metropolitan areas, with quarterly travel to Los Angeles.

Own and evolve end-to-end data pipeline architecture across ingestion, transformation, modeling, and serving. Lead platform improvements for cost efficiency, reliability, access control, observability, orchestration, CI/CD, and developer experience. Drive complex cross-team initiatives, resolve production issues, influence architectural decisions, mentor engineers, and build automation incorporating AI into data engineering workflows.

Leads architecture and hands-on development of scalable data pipelines, governed data models, semantic layers, analytics products, and AI workflows. Writes production SQL and Python, establishes engineering standards, mentors engineers, and drives design reviews. Builds agentic and LLM solutions using retrieval and managed AI services while ensuring data quality, observability, reliability, privacy, and regulatory compliance for PHI and genomic data. Partners with stakeholders, platform teams, and governance forums to deliver trustworthy business solutions.

Designs and develops complex cloud-native data solutions, scalable batch and real-time pipelines, APIs, data models, and enterprise data platforms. Implements data governance, quality, lineage, security, observability, CI/CD, automated testing, and operational reporting capabilities. Collaborates with architects, product owners, analysts, governance, security, and platform teams on technology strategy and cloud modernization. Mentors engineers, leads technical design discussions, presents recommendations, and supports AI-ready data products and emerging development tools.

Designs and maintains scalable Databricks data pipelines on AWS, covering ingestion, transformation, streaming, data quality, and analytics-ready dataset delivery. Builds medallion architecture layers using Spark, Python, SQL, Delta Lake, and AWS services. Responsibilities include source-to-target mapping, unit testing, CI/CD deployment, anomaly analysis, and compliance with FISMA High and multi-tenant governance requirements. Collaborates in Agile teams and uses AI automation tools to accelerate pipeline development and validation.

Designs and maintains scalable data pipelines, warehouse models, and analytics solutions. Builds natural-language data interfaces using GenAI and LLM techniques, optimizes SQL, improves data quality and observability, and supports distributed data platforms on AWS or GCP. Partners with engineering, analytics, business, and product teams, contributes to shared codebases, establishes development standards, and participates in production on-call and incident response.