Chief Data Officer Jobs

About The Position

Requirements

• 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
• Strong hands-on experience with AWS data services (S3, Glue, Lambda, Redshift, etc.)
• Strong hands-on experience with 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.

Nice To Haves

• Experience supporting federal agencies such as SEC, DHS, 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.

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.