Data Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Create logical and physical data models tailored for analytical tasks.

• Construct data structures that unify various applications, databases, files, APIs, logs, and external sources.

• Assess data architectures and propose enhancements for reliability, scalability, performance, maintainability, and accessibility.

• Provide support for data warehouses, data marts, data lakes, lakehouses, and other analytical repositories.

• Design, develop, test, deploy, and maintain ETL/ELT processes and automated data pipelines.

• Extract and assimilate structured and semi-structured data from databases, applications, APIs, flat files, and logs.

• Convert raw transactional data into standardized, analysis-ready datasets.

• Implement scheduling, orchestration, monitoring, error handling, and recovery for data pipelines.

• Create data feeds for reporting, dashboards, analytics platforms, and external systems.

• Clean, normalize, deduplicate, validate, and transform data.

• Investigate data discrepancies and identify root causes alongside technical and business teams.

• Establish data-quality controls.

• Develop curated datasets and models for business intelligence, reporting, visualization, and analytics.

• Collaborate with BI analysts and stakeholders to translate business inquiries into data requirements.

• Assist in defining enterprise metrics and KPIs, along with supporting ad hoc analytical requests.

• Aid analysts with data lineage, relationships, limitations, and appropriate usage.

• Document data sources, transformations, interfaces, business rules, dependencies, lineage, and definitions.

• Keep documentation updated for pipelines, models, interfaces, and analytical repositories.

• Support data dictionaries and metadata repositories.

• Develop and enhance SQL queries, stored procedures, views, and data transformations.

• Analyze and optimize analytical query and data-processing performance.

• Collaborate with DBAs to refine database structures and suggest indexing, partitioning, aggregation, or caching strategies.

• Ensure that analytical workloads do not negatively impact production systems.

• Enforce cybersecurity, privacy, access-control, classification, encryption, auditing, logging, retention, and protection requirements.

• Partner with cybersecurity personnel to ensure compliant data movement and storage.

• Safeguard sensitive information including PII and controlled or restricted data.

• Create maintainable data-engineering code using best software development practices.

• Maintain pipeline code and configurations within version control.

• Engage in peer reviews, testing, configuration management, and change control processes.

• Develop automated tests and support CI/CD and automated deployment processes.

• Troubleshoot production data-processing failures and conduct root-cause analysis.

⛳️ Requirements

• Must be a U.S. Citizen.

• Must be eligible to obtain a Public Trust clearance level.

• Must possess verifiable accredited education.

• Bachelor's degree in Computer Science, Data Science, Information Systems, Software Engineering, Computer Engineering, Mathematics, Statistics, or a related technical field.

• 3–5+ years of professional experience in data engineering, database development, software engineering, BI engineering, or a related technical area.

• Proven experience in designing and developing ETL/ELT processes and data pipelines.

• Strong expertise in relational databases and SQL.

• Experience in integrating data from multiple diverse sources.

• Background in developing analytical data models, data warehouses, or similar repositories.

• Experience in troubleshooting data-quality and data-integration challenges.

• Preferred familiarity with Azure, AWS, Snowflake, Databricks, Spark, Airflow, dbt, Kafka, Power BI, Tableau, or similar platforms.

• Strong proficiency in several of the following: advanced SQL and relational database concepts; Oracle, SQL Server, PostgreSQL, or comparable RDBMS technologies; Python or another data-processing/programming language; ETL/ELT development; data modeling and dimensional modeling; data warehouse architecture; REST APIs and system integration; data validation and quality management; Git or similar version-control systems; automated testing and CI/CD concepts; structured and semi-structured data processing.

• Strong analytical and problem-solving capabilities.

• Ability to grasp complex relationships among applications, databases, interfaces, and business processes.

• Capability to translate business information requirements into technical data solutions.

• Understanding of transactional versus analytical data architectures.

• Knowledge of data lineage, governance, quality, security, and lifecycle management.

• Ability to convey technical concepts to both technical and non-technical audiences.

• Ability to differentiate between data, application, business-process, and analytical challenges.

• Strong commitment to documentation and configuration management practices.

🏝️ Benefits

• Competitive salary and performance-based incentives.

• Comprehensive benefits package including health, dental, and vision insurance.

• Retirement savings plan with company matching.

• Opportunities for professional development and continuous learning.

• Flexible work hours and remote work options.