Analytics Engineer

• Design and develop data serving and semantic layers to support business needs ranging from traditional reporting to reverse ETL and LLM/AI implementations.
• Develop data models for use by analysts and applications from across the business using modern dimensional modeling practices to efficiently serve accurate, consistent data.
• Build and maintain robust, scalable data pipelines to extract, transform, and load (ETL, ELT) data from various sources into the data platform using both batch processing and event-driven patterns.
• Create efficient, performant data models to support business requirements and analytics needs.
• Implement and automate data quality checks and monitoring to ensure data accuracy and drive observability.
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions and on-premises systems.
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis.
• Work closely with data analysts, data scientists, and business stakeholders to understand their data needs and deliver solutions.
• Identify and resolve data-related issues and challenges for technical and non-technical stakeholders.
• Support company-wide projects ranging from server migrations to mergers and acquisitions as a data and systems expert.
• Automate data processes to improve efficiency and reduce manual effort.
• Implement modern concepts like metadata-driven pipelines and Infrastructure-as-Code that will play a key role in a scalable, resilient data platform.
• Document data processes, architecture, and workflows.

• Design and implement complex data pipelines and ETL processes to support cyber investigative capabilities across multi-classification domains
• Architect and develop ETL workflows for highly sensitive data in classified environments
• Support IC Data Services requirements by integrating various intelligence data sources and systems
• Develop and maintain data analytics solutions for desktop and web-based visual analytic applications
• Establish applications that produce manageable, actionable intelligence from streams of structured and semi-structured data
• Design strategies for enterprise database systems and set standards for operations, programming, and security
• Construct and optimize large relational databases across multi-enclave environments (Unclassified, Secret, and Top Secret)
• Tune performance of large-scale data workflows, ensuring cost efficiency, low latency, and high availability
• Design and manage Elasticsearch/Opensearch clusters for fast search, indexing, and retrieval of large-scale datasets
• Integrate new systems with existing warehouse structures and refine system performance and functionality
• Implement CI/CD pipelines for data systems, automate monitoring/alerting, and enforce infrastructure-as-code practices
• Provide technical leadership and mentorship to other team members
• Participate in Program Increments (PIs) and Agile Release Train (ART) activities

ETL Engineer – SME

• Design and implement advanced data visualizations and interactive dashboards that transform complex data into clear, actionable insights, applying strong user experience principles to drive understanding and data-driven decision-making.
• Lead the adoption and integration of Augmented Analytics capabilities, including AI/ML-driven insights, natural language query (NLQ), and natural language generation like “Talk to your Data” features within enterprise BI platforms.
• Lead and evolve self-service analytics (BIaaS) enablement frameworks, providing training, guidance, and reusable assets to empower business users and improve data literacy across the organization.
• Drive enterprise adoption and engagement through Analytics User Communities (Power BI/Tableau User Group etc.), incorporating feedback to continuously improve analytics capabilities and user experience.
• Own and manage the Analytics Service Catalog, ensuring user requests are fulfilled through standardized, governed, and scalable solutions aligned with enterprise priorities.
• Lead modernization efforts by migrating legacy BI assets to enterprise platforms (Power BI/Tableau), improving performance, scalability, and alignment with enterprise data strategy.
• Establish and maintain governance practices for visualization assets, including certification, reuse, lifecycle management, and adherence to data standards.
• Maximize the value of BI solutions by ensuring strong user engagement and platform reliability — tracking usage and performance, managing access/licensing, and providing proactive support to maintain solution integrity.
• Identify opportunities for optimization, automation, and innovation across analytics platforms, user experience, and data consumption patterns.
• Establish and enforce data visualization standards and best practices to ensure consistent, high-quality, user-centric analytics experiences across platforms.
• Provide subject matter expertise in BI and analytics, guiding Agile project teams with technical leadership to drive successful solution delivery.
• Mentor team members and others through Job Rotation/Shadow or skill up program and promote best practices in data visualization, storytelling, and analytics design.

Senior Data Visualization, Augmented Analytics Engineer

• Collaborate with stakeholders to understand business requirements and translate them into scalable and efficient data analytics solutions.
• Possess in-depth knowledge and hands-on experience with Microsoft Fabric ecosystem (Lakehouse, PySpark/SQL notebooks, data pipelines, and deployment pipelines).
• Understanding Microsoft Dynamics as a data source a big plus.
• Design data models that bridge the Fabric Lakehouse, Power BI semantic models, and reporting layers, ensuring consistency between raw, curated, and business-facing data.
• Optimize data models to support efficient data storage, retrieval, and analysis, ensuring data integrity, accuracy, and reliability.
• Knowledge of Fabric capacity (CU) optimization a big plus.
• Implement data integration pipelines using Microsoft Fabric and other relevant tools to extract, transform, and load data from various sources into a unified data platform.
• Create visually appealing and interactive dashboards, reports, and visualizations using Microsoft Power BI and DAX language, enabling users to gain valuable insights from data.
• Identify performance bottlenecks and implement optimizations to improve data processing, query performance, and overall system efficiency.
• Collaborate with cross-functional teams, providing technical leadership and guidance to data engineers, analysts, and other stakeholders to ensure successful project delivery.
• Keep abreast of the latest advancements in data analytics technologies (Microsoft based and others) and industry trends, continuously enhancing your expertise and applying best practices in solution design and implementation.

Data Analytics Engineer

• Partner with leadership to drive strategy
• Collaborate with product, operations, and go-to-market leaders
• Present insights and recommendations to executives
• Own key business metrics and KPIs
• Build scalable data infrastructure
• Create self-service dashboards and reporting tools
• Own high-impact analytical initiatives
• Mentor product managers and operators

Senior Analytics Engineer

• Develop and maintain ETL processes to support data ingestion, transformation, and integration.
• Work with various file formats (TXT, CSV, XML, JSON, YAML) to ensure seamless data processing.
• Implement and optimize ETL workflows using EasyMorph, Databricks, Azure Data Factory, Talend, and Apache Nifi.
• Write clean, efficient, and maintainable code using Python, C#, or Java.
• Ensure data quality, accuracy, and integrity across ETL pipelines.
• Collaborate with data engineers and analysts to define and refine ETL requirements.
• Troubleshoot and resolve ETL failures, ensuring minimal downtime and impact.
• Follow test-driven development (TDD) methodologies to improve reliability and maintainability.
• Maintain and document ETL processes, providing insights and recommendations for improvements.

Associate ETL Engineer

• Lead the end-to-end delivery of complex data engineering projects, including requirements definition with business stakeholders, technical design, implementation oversight, and delivery coordination with data engineering, analytics, and AI/ML teams
• Serve as the primary technical liaison between business partners and engineering teams, translating business requirements into well-scoped, executable data initiatives with clear outcomes
• Architect and develop cloud-native data solutions on AWS, including the design and deployment of data pipelines using AWS Glue, Airflow (Amazon MWAA), Step Functions, Lambda, and related services
• Lead the migration and modernization of existing data workflows, including the conversion of RPA-based automation into cloud-native orchestration solutions using AWS Airflow
• Design, develop, and deploy RESTful and authenticated APIs on Kubernetes (K8s) to expose data services and other reusable data products consumed by downstream systems
• Oversee and contribute to infrastructure-as-code practices using Terraform, ensuring analytics and data engineering workloads are provisioned, managed, and repeatable across environments
• Establish and enforce data pipeline best practices including data quality checks, lineage tracking, and governance standards across all projects under this role's scope
• Monitor, observe, and maintain production data systems and pipelines, using tools such as CloudWatch, DataDog, or Grafana as applicable
• Conduct exploratory and operational data analysis to surface deficiencies, validate pipeline outputs, and support data-driven decision making for business stakeholders
• Define and communicate data architecture standards and patterns to both technical and non-technical audiences, including documentation of architectural decisions and pipeline design
• Collaborate on cross-cutting concerns such as data security, infrastructure alignment, and platform strategy
• Coordinate with DevOps and infrastructure teams to ensure that data engineering deliverables align with CI/CD pipelines, containerization standards, and broader organizational engineering practices

See all jobs on JobTailor