Data Engineer

Anika Systems is seeking a skilled Data Engineer to design, build, and optimize scalable data pipelines and platforms supporting federal clients. This role will play a critical part in enabling enterprise data strategies, supporting Office of the Chief Data Officer (OCDO) initiatives, and delivering high-quality, trusted data for analytics, reporting, and mission operations.

This opportunity is 100% remote.

The ideal candidate has hands-on experience with ETL/ELT pipelines, XBRL data processing, Apache Iceberg-based architectures, and advanced data optimization techniques such as materialized views and context-aware data engineering. This role also requires proficiency in AI tools and AI-assisted development workflows, along with experience building and deploying CI/CD pipelines for data and analytics platforms.

Key Responsibilities

Data Pipeline Development & ETL/ELT

* 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.

Cloud Data Platforms & Iceberg Architecture

* 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.

CI/CD & DataOps Engineering

* 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.

Data Optimization & Performance Engineering

* 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.

XBRL & Financial Data Processing

* 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.

Context Engineering & Data Modeling Support

* 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.

Metadata, Data Catalog, and Governance Integration

* 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.

Stakeholder Collaboration & Agile Delivery

* 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.

Required Qualifications

* 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.

Preferred Qualifications

* 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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