Sr. Data Architect

Sr. Data Architect

Washington, DC 6+ Months Final Interview would be F2F

Enterprise Data Architecture & Data Modeling Expertise

Design and maintain scalable data architectures, data lakes, data warehouses, and logical/physical/conceptual data models that support enterprise analytics and operations. Advanced Data Engineering & ETL/ELT Development Build, optimize, and automate ETL/ELT processes, data integration pipelines, and large-scale data processing frameworks for structured and unstructured data. Strong SQL, Python, and Big Data Experience 7+ years of experience with enterprise data platforms, advanced SQL, Python programming, distributed computing, and big data processing systems. Workflow Automation, Cloud, and DataOps Experience with orchestration tools such as Airflow, Prefect, Dagster, or AWS Step Functions, plus cloud platforms and CI/CD/DataOps practices. Leadership, Troubleshooting & Cross-Functional Collaboration Ability to solve complex data issues, support modernization initiatives, mentor team members, and work closely with business and technical stakeholders to deliver scalable solutions.

Position Responsibilities:

As a Senior Data Engineer / Data Architect, you will play a pivotal role in designing, building, optimizing, and maintaining modern data platforms and data integration solutions that support enterprise analytics, reporting, research, and operational initiatives. You will leverage your expertise in data modeling, database design, and workflow automation to provide scalable, reliable, and efficient data solutions. In this position, you will collaborate with various teams and stakeholders to deliver data-driven technologies that enable organizational growth and data-informed decision-making.

Key Responsibilities Include:

Designing, developing, and maintaining scalable enterprise data architectures, databases, data lakes, and data warehouse solutions. Building, optimizing, and automating ETL/ELT processes as well as data integration pipelines. Developing and maintaining robust data processing frameworks to handle both structured and unstructured data sources. Designing and implementing logical, physical, and conceptual data models tailored to business and analytical requirements. Ensuring data quality, integrity, governance, and security across enterprise data environments. Monitoring and optimizing database performance, storage utilization, and data processing workloads. Developing and implementing workflow orchestration and automation solutions to support data operations and analytics. Collaborating with cross-functional teams to gather requirements and deliver scalable, reliable data solutions. Performing root cause analysis and troubleshooting for data-related issues to enhance operational efficiency. Supporting cloud migration initiatives and modernization projects involving data platforms and pipelines. Implementing and maintaining CI/CD processes, DataOps practices, and deployment automation. Documenting technical designs, architectural decisions, data flows, and operational procedures. Evaluating emerging technologies to recommend and implement improvements to data infrastructure and engineering approaches. Providing technical leadership, guidance, and mentorship to junior members and project teams.

Essential Skills, Experience

Bachelor's degree in Computer Science, Information Technology, Engineering, Mathematics, or a related technical field. At least 7 years of experience in data engineering, data architecture, database administration, or closely related fields. Expertise in designing, developing, and optimizing enterprise data pipelines and integration solutions. Advanced proficiency with SQL and relational database systems (e.g., PostgreSQL, SQL Server, MySQL). Strong programming skills with Python and experience with scripting languages used in data engineering and analytics. Hands-on experience managing large-scale data environments, distributed computing, and big data processing systems. Proficiency with workflow orchestration and automation tools (e.g., Apache Airflow, Prefect, Dagster, AWS Step Functions). Experience developing and supporting data solutions in Linux-based environments. Familiarity with source control and collaboration tools such as GitHub and GitLab. Strong analytical, troubleshooting, and problem-solving abilities. Excellent written and verbal communication skills with the ability to relay technical concepts to diverse audiences.

Preferred Experience/Skills:

Advanced degree in Computer Science, Data Science, Engineering, or a related discipline. Experience with cloud platforms (AWS, Azure, or Google Cloud Platform). Knowledge of Snowflake or other modern cloud data platforms. Experience with NoSQL and graph databases. Experience implementing enterprise data warehouses and Change Data Capture (CDC) methodologies. Background in migrating data platforms and workflows between on-premises and cloud environments. Experience with CI/CD pipelines and DataOps best practices. Experience developing, deploying, and maintaining machine learning solutions. Understanding of time-series data, forecasting models, and advanced analytics. Experience supporting research, financial, regulatory, healthcare, public sector, or other data-intensive domains. Working knowledge of additional programming languages (Java, Scala, JavaScript, Perl, etc.)

Core Competencies:

Data Architecture & Modeling Data Engineering & Integration Database Design & Administration Cloud Data Platforms ETL/ELT Development Workflow Automation Data Governance & Quality DataOps & CI/CD Performance Optimization Problem Solving & Troubleshooting Cross-Functional Collaboration Technical Leadership