Role Description Analytica is seeking a mid-level Data Engineer (Azure) to design, build, and maintain secure, scalable, cloud-based data solutions supporting federal data programs. This role is hands-on and delivery-focused, working within established Azure architectures to develop reliable data pipelines, analytics-ready datasets, and reporting solutions in regulated environments. The Data Engineer will collaborate closely with senior engineers, architects, analysts, and government stakeholders to implement well-defined data requirements and support analytics, dashboards, and operational reporting. Key Responsibilities - Build, maintain, and optimize Azure-based data pipelines and workflows using Azure Data Factory, ADLS Gen2, Synapse Analytics, Azure SQL, Python, and SQL. - Support batch and near–real-time data ingestion, transformation, enrichment, and aggregation to deliver analytics- and reporting-ready datasets. - Implement data quality checks, schema management, and validation to ensure reliable, trusted, and well-documented data. - Develop and maintain analytical data models (e.g., star and snowflake schemas) to support dashboards, reporting, and operational insights. - Contribute to CI/CD pipelines, monitoring, troubleshooting, and performance tuning to ensure reliable data operations. - Implement security controls, access management, and data governance practices aligned with federal compliance requirements. - Work within Agile teams, collaborating closely with senior engineers, analysts, and stakeholders to deliver well-defined data requirements. Qualifications - U.S. citizenship with the ability to obtain a U.S. Federal security clearance. - Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent professional experience). - 3+ years developing and deploying Azure data pipelines for use in machine learning models or supporting data pipelines for such models. - Proficiency in Python for data engineering tasks (Pandas required). Benefits - Competitive compensation with opportunities for bonuses. - Employer-paid health care. - Training and development funds. - 401k match. Company Description Analytica has been recognized by Inc. Magazine as one of the fastest-growing 250 businesses in the US for 3 years. We work with U.S. government clients in health, civilian, and national security missions to build better technology products that impact our day-to-day lives.

• Design scalable, low-latency, and highly reliable data systems • Own the architectural vision for the Market Data Lakehouse • Collaborate closely with teams to enable fast experimentation • Drive performance optimization and long-term scalability • Document architectural decisions and maintain technical roadmaps

• Design, develop, and maintain scalable data models and transformation pipelines using dbt and Snowflake. • Build and manage end-to-end data workflows, from raw ingestion to curated analytical layers. • Develop reusable, modular, and scalable transformations using Jinja and dbt macros. • Select and implement appropriate materializations based on performance and business needs. • Define and implement robust data testing strategies (generic and custom tests). • Ensure high data quality and reliability using tools such as dbt-expectations, dbt-utils, Elementary. • Collaborate with cross-functional teams to understand requirements and deliver data solutions. • Troubleshoot and resolve data quality, performance, and transformation issues. • Contribute to best practices in data modeling, version control, and CI/CD pipelines.

• Design, develop, and maintain scalable data models and transformation pipelines using dbt and Snowflake • Build and manage end-to-end data workflows, from raw ingestion to curated analytical layers • Develop reusable, modular, and scalable transformations using Jinja and dbt macros • Select and implement appropriate materializations (incremental, snapshot, table, view, ephemeral) • Define and implement robust data testing strategies (generic and custom tests) • Ensure high data quality and reliability using tools such as dbt-expectations, dbt-utils, Elementary • Collaborate with cross-functional teams (Data Analysts, Data Scientists, Engineers) to understand requirements and deliver data solutions • Troubleshoot and resolve data quality, performance, and transformation issues • Contribute to best practices in data modeling, version control, and CI/CD pipelines