• Assist in building and maintaining data pipelines using Microsoft Fabric (Data Factory, Lakehouse, OneLake) • Support data ingestion from various sources into analytics-ready datasets • Write and optimize basic to intermediate SQL queries and PySpark • Help prepare data for Power BI reports and dashboards • Perform data validation and quality checks • Troubleshoot simple data pipeline and data issues with guidance • Document data processes and learn platform best practices • Design, develop, and maintain Power BI dashboards and reports • Translate business requirements into data models and visualizations

Title: Senior ETL Developer (SAS/Snowflake) Location: Remote About iWorks: iWorks Corporation, founded in 2005, is a leading provider of information technology and professional services to the federal government. We are a recognized leader in personnel security and vetting solutions, Agile, DevOps, DevSecOps, data analytics, and cloud solutions. Our continuous process improvement approach, combined with our business and technology expertise, results in innovative solutions. Position Overview: The Senior ETL Developers will maintain existing ETL processes and support the migration to Snowflake, using best-in-class practices for performance, security, and maintainability. This role requires architectural expertise, hands-on development expertise, and extensive experience in data modeling, cloud-native integration, and ETL platform reengineering. It involves maintaining legacy SAS-based ETL processes and migrating them to optimized, cloud-native native data processing services The successful candidate will serve as lead data engineer and drive the transformation of Client's SAS based ETL solution, ensuring fidelity of migrated outputs, adherence to federal cybersecurity frameworks, and alignment with best practices in cloud data architecture and governance. Salary Range: 125K - 135K – commensurate with the candidate's skills, experience, location, and qualifications. Key Responsibilities: - Re-engineer and migrate current SAS ETL processes to Snowflake on AWS cloud platform. - Deep expertise Snowflake-native data processing tools (Snowpark, Snow pipe, and Stream) and AWS data processing tools (Glue, AMR, Lambda etc.) - Design and implement secure, performant, and scalable Snowflake data warehouses, including schema structures, access control models (RBAC), and compute optimization patterns. - Design and develop data modeling (e.g., star/snowflake schemas, normalization, denormalization) for analytical and operational use cases. - Reverse engineer and re-platform complex SAS logic into Snowflake SQL, Python, and AWS-native ETL pipelines using tools like Glue, Lambda, and Step Functions. - Implement as per agency governance frameworks: metadata management, data lineage, auditability, data quality monitoring, and tagging aligned to OCC data policies. - Ensure regulatory compliance with FedRAMP, NIST 800-53, and other federal standards for managing PII and financial datasets. - Drive performance tuning and cost optimization strategies for Snowflake compute warehouses and AWS resources, including query pruning, clustering, and storage tiering. - Lead architectural reviews and technical design sessions, overseeing development, validation, cutover, and production readiness of re-platformed ETL pipelines. - Collaborate cross-functionally with DevSecOps engineers, DBAs, data stewards, and cybersecurity stakeholders to ensure system resilience, security, and alignment with enterprise architecture. - Maintain and monitor current SAS ETL jobs until it is migrated to Snowflake ETL solutions. Required Skills & Qualifications: - Clearance: Public Trust, open to upgrade if necessary - 10 plus years of ETL development experience with strong SQL and Python skills. - Deep implementation experience with Snowflake and cloud-based ETL tools (AWS Glue, lambda, S3, cloudwatch). - Extensive hands-on experience with Snowflake design, performance tuning, and security configuration. - Proven expertise in migrating SAS-based ETLs to Snowflake and/or cloud-native platforms, with working knowledge of output validation techniques. - Deep understanding of dimensional modeling, normalized schema design, and metadata-driven ETL architecture. - Experience with CI/CD pipelines, Terraform, and source control systems (GitHub, Bitbucket). - Strong knowledge of data privacy, compliance, and auditing requirements in federal environments (FISMA, FedRAMP, NIST). - Ability to lead technical design efforts, communicate complex architectural concepts, and influence stakeholders. Preferred Experience: - Experience in supporting federal financial agencies like FIDC, Treasury. - Familiarity with SAS Enterprise Guide, PROC SQL, and legacy ETL performance profiling. - Familiarity with Perl scripts. - Background in data platform modernization, data governance, or cloud cost management. Please Note: We maintain an on-camera policy for all virtual company meetings to foster engagement and collaboration. Reasonable exceptions may be granted with prior approval from Human Resources and/or the applicable manager or client. FLSA & EMPLOYMENT STATUS: FLSA EXEMPT AND FULL-TIME POSITION iWorks Corporation is an Equal Employment Opportunity/Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, religion, sex, national origin, disability, Veteran status, sexual orientation, or other protected characteristic. iWorks is committed to maintaining a safe and productive work environment for all employees and ensuring the security and well-being of our clients. As part of our standard hiring process, we may conduct background checks and drug screenings on potential candidates to assess their suitability for employment.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a Senior Analytics Engineer to join our Product Lifecycle Analytics team, supporting cutting-edge electro-mechanical systems operating around the world. In this role, you will leverage your deep engineering expertise and strong statistical foundation to help develop and mature an end-to-end digital thread across a portfolio of Collins Aerospace products. As a senior member of the team, you will play a critical role in integrating data across historically siloed business units, transforming complex engineering and operational data into actionable insights. Your work will directly influence product performance, service optimization, and enterprise business intelligence strategies. This position requires a strategic thinker who can bridge engineering knowledge with advanced analytics, drive cross-functional collaboration, and lead initiatives that elevate data-driven decision-making across the organization. What You Will Do - Develop a strong understanding of core electrical and mechanical system principles - Conduct data analysis with Python to drive informed business decisions - Apply statistical methods to assess data completeness, accuracy, and reliability - Support engineering teams with root cause analysis investigations - Promote the adoption of semantic models and ontologies across the organization - Design and build executable Python-based analytical models - Automate data processing, analysis, and formatting workflows - Clean, structure, and analyze datasets using Python - Organize data and implement standardized labeling practices in AWS S3 - Deploy machine learning algorithms on both numerical and categorical data sets Qualifications - Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and minimum 5 years prior relevant experience or an Advanced Degree in a related field and minimum 3 years of experience Requirements - Background in Data Science, Statistics, or an Engineering discipline - Proficiency in Python for data analysis and model development - Experience within the Aerospace & Defense industry or Information Systems environments - Familiarity with graph databases, semantic layers, and data ontology frameworks - Hands-on experience with cloud and distributed technologies such as AWS, Azure, or Apache Spark - Knowledge of Git or similar version control systems, following software development best practices - Understanding of the full software development lifecycle, from concept through deployment - Experience using Agile collaboration and project management tools such as Jira and Confluence Benefits - Medical, dental, and vision insurance - Three weeks of vacation for newly hired employees - Generous 401(k) plan that includes employer matching funds and separate employer retirement contribution, including a Lifetime Income Strategy option - Tuition reimbursement program - Student Loan Repayment Program - Life insurance and disability coverage - Optional coverages you can buy: pet insurance, home and auto insurance, additional life and accident insurance, critical illness insurance, group legal, ID theft protection - Birth, adoption, parental leave benefits - Ovia Health, fertility, and family planning - Adoption Assistance - Autism Benefit - Employee Assistance Plan, including up to 10 free counseling sessions - Healthy You Incentives, wellness rewards program - Doctor on Demand, virtual doctor visits - Bright Horizons, child and elder care services - Teladoc Medical Experts, second opinion program - And more!

• Build and own core operational data models in our enterprise data warehouse that power DC performance tracking and operational monitoring. • Design scalable transformation logic using dbt and SQL to support use cases such as hourly employee activity tracking, throughput analysis, labor productivity, and composite performance models (e.g., “perfect order” frameworks). • Partner directly with DC Operations and Customer Experience leaders to translate operational workflows into measurable, reliable data assets. • Define and implement trusted metrics for operational efficiency, service quality, workforce performance, and fulfillment reliability. • Ensure operational data pipelines are tested, documented, and production-grade (balancing performance, scalability, and long-term maintainability). • Support deeper modeling efforts (e.g., capacity planning, forecasting, optimization) in collaboration with Data Science partners. • Act as a senior technical voice for operational analytics engineering, influencing modeling standards, patterns, and best practices. • Operate independently as the primary data infrastructure owner for operational performance initiatives.