Top Senior Data Engineer Jobs in Huntsville, AL

Plans and executes enterprise data migrations, conversions, transformations, archival, and cloud data platform operations. Builds ETL/ELT pipelines and migration scripts, performs profiling and source-to-target mapping, validates data, manages cutovers and rollback procedures, and troubleshoots migration issues. Optimizes cloud platforms, databases, workloads, security, access, cost, and performance while collaborating with engineering, governance, architecture, and business teams.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.

Design, build, and operate scalable ETL/ELT pipelines and backend data services using Go. Integrate relational, NoSQL, blockchain, API, web, and social data sources; develop NLP pipelines for text extraction and classification; and support production APIs, dashboards, alerts, and intelligence systems. Collaborate with engineering, data science, and investigations teams on architecture, data quality, lineage, validation, monitoring, and client-facing products.

Leads data engineering and data science teams, setting strategy and delivering scalable pipelines, data architecture, models, analytics infrastructure, and data governance. Partners with product, analytics, and business stakeholders to prioritize initiatives, assess upstream changes, maintain data quality, and support Snowflake-based warehousing. This hands-on leadership role includes mentoring staff, contributing to technical codebases, and advancing data science and engineering solutions.

Design, build, and maintain scalable batch and streaming data pipelines using Python, SQL, Kafka, and GCP. Develop REST and GraphQL data services, CI/CD automation, observability, and data-quality frameworks. Integrate ML and NLP models into production workflows, including continuous retraining and real-time inference. Support structured and unstructured healthcare datasets, distributed systems, microservices, and reliable data infrastructure while collaborating with data science and MLOps teams.

Build and configure a government Databricks workspace, develop ingestion and integration pipelines from enterprise and legacy systems, and prepare data for reporting. Implement Unity Catalog governance, metadata management, auditing, and lineage; configure MLflow workflows; assess security controls for sensitive data; and create architecture documentation, data dictionaries, and SOPs to support long-term sustainment.

Lead the design and optimization of healthcare data pipelines integrating EHR, payer, HIE, and vendor data. Build AWS-based ETL/ELT infrastructure, reusable integration tooling, data quality monitoring, and ML enablement foundations. Ensure HIPAA-compliant handling of clinical data, including security controls, audit logging, encryption, and de-identification. Provide technical leadership, mentor engineers, influence platform architecture, and lead complex partner integrations involving FHIR, HL7v2, CCDA, and healthcare terminology standards.

Develops advanced data engineering and visual analytics applications for structured and semi-structured data. Designs enterprise database systems, builds large relational databases, develops and optimizes data pipelines, integrates systems with existing data warehouses, and improves system performance. Requires Java, AWS, ETL, Spark, Scala, SQL, PostgreSQL, NiFi, Informatica, and Linux experience, along with an active Top Secret/SCI-eligible clearance.

Architect, build, and operate scalable data platforms processing billions of daily events through real-time and batch pipelines. Own production reliability, data quality, security, observability, and performance while partnering with engineering, analytics, data science, growth, and business teams. Develop frameworks and datasets, make architectural decisions, evaluate technologies, mentor engineers, and help define the platform roadmap.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.

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.

Leads the architecture, development, operation, validation, and optimization of high-volume ETL and data migration pipelines. Transforms structured and unstructured data from legacy systems into cloud platforms, troubleshoots failures, performs data quality validation, conducts root cause analysis, and documents migration processes. Collaborates with cloud, DevSecOps, database, QA, and product teams while reporting pipeline performance, migration status, and data quality metrics.

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.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.

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.

Design and implement scalable analytics/data models and dbt pipelines, manage Databricks, build dashboards and BI solutions, partner with business stakeholders, support CRM/marketing analytics, and explore GenAI/ML opportunities to deliver reliable, automated data products.

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.

Build and scale Benepass’s data platform, including warehouses, replication pipelines, orchestration, dbt transformations, Cube semantic models, and governed analytics infrastructure. Own data quality, security, compliance, reliability, performance, cost optimization, and documentation. Partner with Product, Engineering, Operations, GTM, Finance, analysts, and data scientists to deliver trusted reporting and data solutions. Shape the platform roadmap, mentor colleagues, and drive architectural improvements as a senior individual contributor.

Build and scale Benepass’s data platform, including warehouses, replication pipelines, orchestration, dbt transformations, semantic models, and data quality systems. Own platform reliability, governance, security, performance, cost optimization, and developer experience. Partner with Product, Engineering, Operations, GTM, Finance, analysts, and data scientists to deliver trusted reporting and analytics infrastructure. Shape the data platform roadmap, evaluate new technologies, and mentor cross-functional partners as a senior individual contributor.