Data Engineer (Azure)

Bethesda, MD
Full Time
Mid Level

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. 

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. The company offers competitive compensation with opportunities for bonuses, employer-paid health care, training and development funds, and 401k match.   

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. 

Required 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). 

Analytica LLC is an Equal Opportunity Employer. We are committed to providing equal employment opportunities to all individuals, regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other characteristic protected by applicable federal, state, or local law. As a federal contractor, we comply with the Vietnam Era Veterans' Readjustment Assistance Act (VEVRAA) and take affirmative action to employ and advance in employment qualified protected veterans. We ensure that all employment decisions are based on merit, qualifications, and business needs. We prohibit discrimination and harassment of any kind. Analytica LLC also provides reasonable accommodations to applicants and employees with disabilities, in accordance with applicable law.

To enhance efficiency, fairness, and accuracy, Analytica may use AI-assisted tools to support certain aspects of our hiring process.

  • Application Review: AI tools may help identify skills and experiences relevant to the role.
  • Interview Support: AI-powered notetaking tools may be used during interviews to document discussions and summarize key points.

These tools are used to assist our team. All hiring decisions are made by Analytica recruiters and hiring managers.

By submitting an application, you acknowledge that AI-assisted tools may be used to support parts of the application and interview process.

When receiving email communication from Analytica, please ensure that the email domain is analytica.net to verify its authenticity.

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