{"id":1550432,"url":"https://alion.io/job/strategic-staffing-solutions-lead-data-engineer","title":"Lead Data Engineer","company":{"id":2027987,"name":"Strategic Staffing Solutions","domain":"strategicstaff.com","url":"https://alion.io/company/strategicstaff","size_band":"5000+","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"B","score":75,"open_postings":7,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":22,"computed_at":"2026-10-12T06:55:09Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Charlotte, United 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workflows.\nArchitect and oversee enterprise data lake and data warehouse solutions using AWS Lake Formation, Amazon Redshift, Amazon Athena, S3, and related AWS technologies.\nCollaborate with Lead Developers, Data Scientists, Architects, Product Owners, and business stakeholders to define technical strategy and scalable data solutions.\nProvide hands-on technical leadership, engineering oversight, code reviews, and mentorship to Data Engineers and development teams.\nDrive architectural decisions in partnership with Data and Solution Architects to ensure scalability, security, reliability, performance, and maintainability.\nDesign and support Kafka-based streaming and event-driven data architectures, preferably using Confluent Kafka.\nDevelop distributed data processing solutions using Python, PySpark, AWS EMR, and other cloud-native technologies.\nEstablish engineering standards and best practices for data modeling, ETL frameworks, pipeline reliability, monitoring, observability, and operational excellence.\nLead end-to-end solution delivery while ensuring alignment with business requirements, enterprise architecture standards, security controls, and regulatory requirements.\nDesign and implement Infrastructure as Code solutions using Terraform across multiple AWS accounts and environments.\nDevelop and maintain CI/CD frameworks using GitHub and GitHub Actions.\nOversee production support and operational management of AWS-based data platforms, including root-cause analysis, troubleshooting, and performance optimization.\nChampion data governance, metadata management, data quality, observability, and data stewardship practices across platforms and teams.\nIdentify opportunities to modernize data architecture and improve operational efficiency through automation and cloud-native technologies.\nSupport the development of AI-ready data pipelines and machine learning workflows, including feature engineering and MLOps.\nDesign data solutions capable of supporting Generative AI applications, intelligent search, RAG architectures, LLMs, and vector databases.\nDrive technical decision-making and clearly communicate complex data architecture concepts to both technical and non-technical stakeholders.\nRequired Qualifications\n8+ years of Data Engineering experience, including at least 5+ years of extensive experience working within AWS environments.\nExpert-level experience with AWS services including S3, EMR, Glue Jobs, Lambda, Athena, CloudTrail, SNS, SQS, CloudWatch, and Step Functions.\nStrong hands-on experience designing and implementing enterprise-scale data lakes and data warehouses using AWS Lake Formation, Amazon Redshift, and Amazon Athena.\nAdvanced Python development experience with extensive hands-on use of PySpark.\nAdvanced SQL expertise, including query optimization, large-scale data processing, and enterprise data warehousing.\nStrong data modeling experience, including dimensional modeling, Data Vault, and other enterprise data modeling techniques.\nDeep experience designing, developing, and optimizing scalable and resilient data pipelines within AWS environments.\nStrong experience with distributed data processing frameworks, particularly PySpark and AWS EMR.\nExtensive knowledge of database management, performance tuning, and data architecture best practices.\nExperience with Kafka-based streaming architectures, preferably Confluent Kafka.\nExpertise with Infrastructure as Code using Terraform.\nExperience designing and implementing CI/CD frameworks using GitHub and GitHub Actions.\nDeep knowledge of AWS IAM roles, policies, governance, security controls, and cloud security best practices.\nStrong experience with workflow orchestration platforms such as AWS Step Functions, Apache Airflow, or equivalent technologies.\nExperience leading cloud migration, modernization, or enterprise data platform initiatives.\nStrong understanding of data governance, metadata management, data quality frameworks, observability, resiliency, and operational supportability.\nExperience creating AI applications using AWS Bedrock.\nExperience building AI-ready data pipelines and ML workflows, including feature engineering and MLOps.\nKnowledge of Generative AI technologies, LLMs, Retrieval-Augmented Generation (RAG), and vector databases.\nExperience working with cloud-based AI and data platforms such as AWS, Azure, or Databricks.\nAbility to lead hands-on development efforts while providing technical direction, engineering oversight, and code reviews across multiple initiatives.\nExperience establishing cloud development environments, infrastructure standards, security controls, and migration strategies across multiple accounts and environments.\nProven ability to identify data gaps, develop strategic remediation plans, and implement scalable automation solutions.\nExperience designing highly reliable data pipelines with a strong emphasis on data quality, observability, resiliency, and operational 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