Data Engineering Lead

Position OverviewThe Data Engineering Lead is responsible for designing and implementing modern, scalable data architectures to support migration of legacy, file-based analytical systems to AWS Cloud Native environments.This role leads the transformation of legacy SAS-based data storage models—including flat files, batch outputs, and subsystem-specific data artifacts—into structured, governed, and scalable data models optimized for cloud-native processing.The Data Engineering Lead will ensure data integrity, performance, and visibility across a system-of-systems modernization initiative, while providing technical leadership for data modeling, ingestion patterns, validation frameworks, and transparency reporting.Expert-level proficiency in Python and strong experience designing AWS-based data architectures are required.Key ResponsibilitiesLegacy Data Discovery & Data Model TransformationParticipate in structured system inventory efforts to document:Legacy file-based storage structuresSAS dataset dependenciesSubsystem data flowsManual gating and handoff processesAnalyze legacy storage models and design target-state data models aligned to AWS Cloud Native architecture.Replace file-driven batch dependencies with:API-based ingestionEvent-driven workflowsDatabase-backed storage (e.g., Aurora/Postgres)Define canonical data schemas and transformation standards.Cloud-Native Data Architecture DesignArchitect scalable AWS data pipelines using services such as:S3GlueLambdaEventBridgeSNS/SQSAurora/PostgresBatchAthenaDesign data ingestion, staging, transformation, and validation workflows.Establish schema management, versioning, and data lineage practices.Optimize data storage for performance, scalability, and cost efficiency.Support serverless and containerized data processing architectures.Expert Python-Based Data EngineeringDevelop advanced Python-based data transformation and validation pipelines.Implement modular, reusable data processing components.Optimize large-scale data manipulation for distributed execution.Develop high-performance ETL/ELT frameworks.Embed automated validation checks directly into data pipelines.Expert-level Python proficiency is required, particularly for:High-volume data processingData validation logicModular data engineering frameworksData Accuracy, Validation & VisibilityDesign and implement automated data validation frameworks to ensure:Functional equivalence during migrationRecord-level and aggregate-level consistencyDownstream compatibility across subsystemsDevelop dashboards and reporting mechanisms providing:Data accuracy metricsPipeline health indicatorsVariance detection summariesEnable transparency into data transformation impacts across modernization phases.Support regression validation through golden datasets and automated comparisons.System-of-Systems Data CoordinationCoordinate with Senior Developers and Requirements Engineers to align data models with application modernization.Ensure upstream/downstream data contract stability.Prevent data thrashing during phased migration.Support orchestration of gated workflows through automated triggers rather than manual file exchanges.Collaborate across workstreams to establish shared data standards.DevSecOps & Governance AlignmentIntegrate data pipelines into CI/CD frameworks.Support infrastructure-as-code alignment (Terraform/CloudFormation collaboration).Ensure compliance with security controls (IAM, encryption, key management).Produce documentation supporting:Architecture review boardsInterface control documentsData flow diagramsSupport ATO-related data validation evidence.RequirementsRequired Qualifications8+ years of experience in data engineering or data architecture.Expert-level proficiency in Python for data engineering.Demonstrated experience transforming legacy file-based systems into cloud-native data architectures.Experience developing data models for high-volume, data-intensive applications.Deep experience with AWS data services (Glue, Lambda, S3, Aurora/Postgres, EventBridge, etc.).Experience designing scalable ETL/ELT pipelines.Experience building analytical dashboards (e.g., QuickSight or equivalent).Experience implementing automated data validation and quality controls.Experience working in Agile Scrum Teams.U.S. Citizenship required.Preferred QualificationsExperience modernizing SAS-based data environments.Experience supporting system-of-systems integration programs.Experience implementing data lineage and metadata management.Experience operating in regulated or federal environments.Key CompetenciesSystems-level thinking across data ecosystemsStrong schema design and normalization expertiseData accuracy and integrity focusAutomation-first mindsetCross-workstream coordination capabilityBenefits401(k) with matching and 100% VestedHealth Insurance - 3 plans to select fromDental insuranceVision InsuranceHealth savings accountLife insuranceShort Term DisabilityLong Term DisabilityAD&DPaid time offProfessional development assistanceTrainingTuition reimbursementFlexible scheduleFlexible spending accountReferral programPaid Legal Planand more....Ignite IT is an Equal Employment Opportunity/Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, religion, sex, national origin, disability, Veteran status, sexual orientation, or other protected characteristic. In accordance with EO 13665 Final Rule, Ignite IT will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant.Applicants selected may be required to possess and maintain a government clearanceUS CITIZENSHIP REQUIRED