[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.

Responsibilities

• Design, develop, and maintain robust ETL/ELT pipelines to ingest, transform, and deliver data across enterprise platforms
• Build scalable data ingestion frameworks for structured and semi-structured data, including XBRL filings and financial datasets
• Implement data transformation logic to support analytics, reporting, and regulatory use cases
• Ensure data pipelines are reliable, performant, and scalable in cloud environments
• Leverage AI-assisted development tools to accelerate pipeline development, testing, and optimization
• Develop 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 lakes
• Support lakehouse architectures that unify data lakes and data warehouses
• Optimize data storage and retrieval strategies for performance and cost efficiency
• Enable data platforms that support AI/ML workloads and downstream generative AI use cases
• Design and implement CI/CD pipelines for data pipelines, infrastructure, and analytics code using tools such as GitHub Actions, GitLab CI, Jenkins, or AWS-native services
• Automate build, test, and deployment processes for ETL pipelines and data platform components
• Implement DataOps best practices, including version control, automated testing, environment promotion, and rollback strategies
• Ensure reproducibility, reliability, and governance of data pipeline deployments across environments
• Integrate AI-driven testing and monitoring tools to improve pipeline quality and reduce operational risk
• Design and implement materialized views and other performance optimization techniques to improve query efficiency
• Tune data pipelines and queries for performance, scalability, and cost
• Implement partitioning, indexing, and caching strategies aligned to workload patterns
• Develop pipelines to ingest, parse, and normalize XBRL (eXtensible Business Reporting Language) data
• Support regulatory and financial data use cases requiring high accuracy and traceability
• Ensure alignment with data standards and validation rules for financial reporting datasets
• Apply context engineering principles to ensure data is enriched with meaningful metadata, lineage, and business context
• Collaborate with Data Architects to support data modeling, schema design, and entity relationships
• Enable downstream analytics and AI use cases by structuring data for usability, discoverability, and governance
• Integrate pipelines with enterprise data catalogs and metadata management systems
• Support automated metadata capture, lineage tracking, and data quality monitoring
• Ensure alignment with data governance frameworks and standards established by OCDO organizations, including AI data readiness and traceability
• Collaborate with data architects, analysts, and business stakeholders to understand data needs and deliver solutions
• Participate in stakeholder listening campaigns, workshops, and data discovery efforts
• Work in Agile teams to iteratively deliver data capabilities and enhancements
• Contribute to identifying and implementing AI-driven efficiencies and automation opportunities across the data lifecycle

Skills

• Bachelor's degree in Computer Science, Engineering, Data Science, or related field
• 5+ years of experience in data engineering, ETL development, or data platform engineering
• Strong hands-on experience with: ETL/ELT tools and frameworks, AWS data services (S3, Glue, Lambda, Redshift, etc.), Apache Iceberg and modern data lake architectures
• Experience designing and implementing CI/CD pipelines for data platforms and ETL workflows
• Demonstrated 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 datasets
• Proficiency in SQL and Python
• Experience implementing materialized views and query optimization techniques
• Understanding of data modeling concepts and metadata management
• Familiarity with data governance, data quality practices, and data readiness for AI/ML use cases
• Ability to work in Agile, DevOps-oriented environments
• U.S. Citizenship required; ability to obtain and maintain a federal clearance
• Experience supporting federal agencies such as SEC, DHS, Treasury, or Federal Reserve System
• Familiarity with data catalog tools (e.g., Collibra, Alation, ServiceNow)
• Experience with Apache Spark, Kafka, or other distributed data processing frameworks
• Experience enabling data pipelines for AI/ML or generative AI applications
• Knowledge of data maturity frameworks (e.g., EDM DCAM, TDWI)
• Exposure to context engineering or semantic data layer design
• AWS or data engineering certifications
• Experience with infrastructure-as-code (IaC) tools (e.g., Terraform, CloudFormation) in support of CI/CD pipelines

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