[Remote] Data Engineer

Note: The job is a remote job and is open to candidates in USA. Anika Systems is seeking a skilled Data Engineer to design, build, and optimize scalable data pipelines and platforms supporting federal clients. The role involves developing ETL/ELT pipelines, managing cloud data platforms, and implementing CI/CD processes to ensure reliable data delivery and governance.ResponsibilitiesDesign, develop, and maintain robust ETL/ELT pipelines to ingest, transform, and deliver data across enterprise platformsBuild scalable data ingestion frameworks for structured and semi-structured data, including XBRL filings and financial datasetsImplement data transformation logic to support analytics, reporting, and regulatory use casesEnsure data pipelines are reliable, performant, and scalable in cloud environmentsLeverage AI-assisted development tools to accelerate pipeline development, testing, and optimizationDevelop and manage data solutions leveraging AWS services (e.g., S3, Airflow, DAGs, Glue, Lambda, Redshift)Implement and optimize Apache Iceberg table formats for large-scale, ACID-compliant data lakesSupport lakehouse architectures that unify data lakes and data warehousesOptimize data storage and retrieval strategies for performance and cost efficiencyEnable data platforms that support AI/ML workloads and downstream generative AI use casesDesign and implement CI/CD pipelines for data pipelines, infrastructure, and analytics code using tools such as GitHub Actions, GitLab CI, Jenkins, or AWS-native servicesAutomate build, test, and deployment processes for ETL pipelines and data platform componentsImplement DataOps best practices, including version control, automated testing, environment promotion, and rollback strategiesEnsure reproducibility, reliability, and governance of data pipeline deployments across environmentsIntegrate AI-driven testing and monitoring tools to improve pipeline quality and reduce operational riskDesign and implement materialized views and other performance optimization techniques to improve query efficiencyTune data pipelines and queries for performance, scalability, and costImplement partitioning, indexing, and caching strategies aligned to workload patternsDevelop pipelines to ingest, parse, and normalize XBRL (eXtensible Business Reporting Language) dataSupport regulatory and financial data use cases requiring high accuracy and traceabilityEnsure alignment with data standards and validation rules for financial reporting datasetsApply context engineering principles to ensure data is enriched with meaningful metadata, lineage, and business contextCollaborate with Data Architects to support data modeling, schema design, and entity relationshipsEnable downstream analytics and AI use cases by structuring data for usability, discoverability, and governanceIntegrate pipelines with enterprise data catalogs and metadata management systemsSupport automated metadata capture, lineage tracking, and data quality monitoringEnsure alignment with data governance frameworks and standards established by OCDO organizations, including AI data readiness and traceabilityCollaborate with data architects, analysts, and business stakeholders to understand data needs and deliver solutionsParticipate in stakeholder listening campaigns, workshops, and data discovery effortsWork in Agile teams to iteratively deliver data capabilities and enhancementsContribute to identifying and implementing AI-driven efficiencies and automation opportunities across the data lifecycleSkillsBachelor's degree in Computer Science, Engineering, Data Science, or related field5+ years of experience in data engineering, ETL development, or data platform engineeringStrong hands-on experience with: ETL/ELT tools and frameworks, AWS data services (S3, Glue, Lambda, Redshift, etc.), Apache Iceberg and modern data lake architecturesExperience designing and implementing CI/CD pipelines for data platforms and ETL workflowsDemonstrated proficiency using AI tools and AI-assisted development workflows (e.g., LLM copilots, automated code generation, pipeline optimization tools)Experience processing XBRL or complex financial/regulatory datasetsProficiency in SQL and PythonExperience implementing materialized views and query optimization techniquesUnderstanding of data modeling concepts and metadata managementFamiliarity with data governance, data quality practices, and data readiness for AI/ML use casesAbility to work in Agile, DevOps-oriented environmentsU.S. Citizenship required; ability to obtain and maintain a federal clearanceExperience supporting federal agencies such as SEC, DHS, Treasury, or Federal Reserve SystemFamiliarity with data catalog tools (e.g., Collibra, Alation, ServiceNow)Experience with Apache Spark, Kafka, or other distributed data processing frameworksExperience enabling data pipelines for AI/ML or generative AI applicationsKnowledge of data maturity frameworks (e.g., EDM DCAM, TDWI)Exposure to context engineering or semantic data layer designAWS or data engineering certificationsExperience with infrastructure-as-code (IaC) tools (e.g., Terraform, CloudFormation) in support of CI/CD pipelinesCompany OverviewAnika Systems provides data and analytics, artificial automation, cloud engineering, application development & enterprise IT modernization. It was founded in 2005, and is headquartered in Leesburg, Virginia, USA, with a workforce of 51-200 employees. Its website is https://www.anikasystems.com/.