Top Data Engineer Jobs in Huntsville, AL

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Develop data analytics and visual analytic applications using structured and semi-structured data. Design enterprise databases, build relational database systems, integrate data systems, and optimize data pipelines and performance. The role uses Java, AWS, Spark, Scala, SQL, PostgreSQL, NiFi, Informatica, Linux, and ETL technologies to support actionable intelligence applications. An active Top Secret/SCI-eligible clearance is required, with potential CI polygraph eligibility.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Develop and optimize data pipelines, enterprise databases, and analytics applications using relational and semi-structured data. Responsibilities include designing database strategies, integrating systems, improving performance, and enabling actionable intelligence through visualization and high-performance computing. The role requires Java, AWS, ETL, Spark/Scala, SQL, PostgreSQL, NiFi, Informatica, Linux, and an active Top Secret/SCI-eligible clearance, with potential CI polygraph eligibility.

Staff Data Engineer will set technical direction and build Eve’s scalable data platform. Responsibilities include Snowflake administration, role-based access, governance, ingestion, dbt modeling and orchestration, Terraform infrastructure, observability, schema change detection, reliability practices, and CI standards. The role also mentors engineers and analysts, supports AI-assisted development, and establishes secure, reliable workflows for product, case, customer, and go-to-market data.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Designs, develops, and maintains scalable data warehouses, data lakes, platforms, and ETL/ELT pipelines. Builds batch and streaming workflows, processes structured and unstructured data, optimizes cloud storage and pipeline performance, and implements data governance and lifecycle management. Guides multidisciplinary teams in agile environments while delivering mission-focused data engineering solutions.

Design, build, optimize, and operate scalable ETL/ELT pipelines supporting petabyte-scale legacy-to-cloud data migration. Develop data models, mappings, schemas, and data quality processes for structured and unstructured datasets. Improve automation, performance, reliability, and fault tolerance while supporting AWS security and compliance requirements. Collaborate with QA, Cloud, DevSecOps, and migration teams; document workflows; track migration progress; and mentor engineers. This is a full-time, on-site role in Huntsville, Alabama requiring active Top Secret clearance.

Designs and develops advanced data engineering and visual analytics applications using structured and semi-structured data. Responsibilities include building large relational databases, developing analytics for desktop and web applications, integrating systems with data warehouses, optimizing performance, and establishing database operation, programming, and security standards. The role requires expertise in Python, Java or Scala, AWS, NLP, ML model evaluation, Git, and GitLab CI/CD, plus an active Top Secret/SCI clearance and potential CI polygraph eligibility.

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.

Builds and maintains scalable data pipelines, ETL/ELT infrastructure, analytics platforms, and data delivery frameworks. Responsibilities include data modeling, SQL optimization, quality assurance, governance, troubleshooting, cloud-based big data processing, and stakeholder support. The role also improves data infrastructure scalability, documents processes, educates users on data literacy, and provides guidance to junior staff. Requires a bachelor’s degree or equivalent experience and at least four years of relevant data engineering or analytics experience.

Develops enterprise data pipelines, streaming ingestion systems, AWS data lakes, Snowflake warehouses, analytics solutions, and MarkLogic integrations. Designs data and software architectures, REST APIs, and JavaScript modules while supporting Agile software development. Requires extensive experience with AWS data services, Python, JavaScript, Java, databases, ETL, web development, data integration, and enterprise information architecture.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Build and operate production pipelines for legal records from PACER, court systems, opinions, PDFs, scans, XML, and HTML. Develop LLM-assisted extraction workflows, validate data quality, optimize SQL, and support RAG and semantic search products. Manage AWS S3 and RDS systems within security guardrails while collaborating with engineering teams. The role requires strong Python and SQL skills, legal data experience, document extraction expertise, and autonomous problem-solving with large, messy datasets.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Architects, builds, tests, and maintains scalable data infrastructure and ELT/ETL pipelines using AWS Glue, Glue Catalog, and Step Functions. Performs data modeling, SQL querying, data migration, schema optimization, validation, and data quality improvements. Partners with analytics and business teams to transform raw data into production-ready datasets. The role may also support AI/ML pipelines, governance, access controls, DevOps workflows, and real-time data architectures.

Build and maintain data pipelines for network and operational telemetry, integrate DataMiner and other monitoring systems, create Power BI dashboards, configure monitoring metrics and alerts, automate reporting, analyze network performance and recovery events, and translate technical findings into actionable insights for technical and executive stakeholders.

Build and maintain production-grade batch and streaming data pipelines in Databricks using PySpark and SQL. Develop ingestion frameworks, data models, APIs, data quality checks, monitoring, and Medallion Architecture across bronze, silver, and gold layers. Integrate diverse data sources, optimize Spark workloads, document data lineage and architecture, and collaborate with engineering and federal stakeholders. An active Secret clearance or higher is required.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.