Senior Data Engineer

As a Senior Data Engineer at Dark Wolf Solutions, you will be entrusted with the design, development, and optimization of data pipelines and databases essential for operational visibility and security. Your key responsibilities will include building scalable data pipelines, administering mission data services, developing real-time integration workflows, and ensuring database performance and security. You will also lead troubleshooting efforts for data flows and mentor junior engineers to bolster our team's expertise.

To be considered for the Senior Data Engineer role at Dark Wolf Solutions, candidates should possess a Bachelor's degree in Computer Science or a related field and have 6-8 years of experience in data engineering or platform operations. Hands-on experience with relational databases like PostgreSQL and expertise in data pipeline tools such as Apache NiFi or Airflow are essential. Additionally, an active Secret clearance, proficiency in programming languages like Python or SQL, and familiarity with cloud environments are required.

The work environment at Dark Wolf Solutions for a Senior Data Engineer is a collaborative hybrid model, combining both remote and on-site support. On-site presence is essential for classified system activities, fostering team interaction and collaboration. The culture encourages innovation, with a focus on mission-critical operations where every team member plays a crucial role in achieving data visibility and security across environments.

For a Senior Data Engineer at Dark Wolf Solutions, advanced skills in big data technologies like Hadoop and Spark can be incredibly beneficial, along with certifications like AWS Certified Data Analytics – Specialty. Familiarity with the Elastic Stack, data visualization tools, and DevSecOps practices can further enhance your capabilities in supporting our mission objectives and ensuring data integrity across diverse environments.

The salary range for the Senior Data Engineer position at Dark Wolf Solutions is estimated between $140,000.00 and $175,000.00, based on experience, technical expertise, and relevant certifications. This competitive compensation shows our commitment to attracting top talent to enhance our cyber operations.

When designing data pipelines for classified environments, I prioritize security and compliance by adhering to NIST standards and relevant encryption protocols. I first assess the data sources and ingestion methods to ensure the pipeline supports both real-time and batch processing while maintaining data integrity. Documentation and consistent monitoring are critical to optimize performance and troubleshoot potential issues effectively.

In my previous role, I implemented ETL processes to ingest and transform operational data from diverse sources. I utilized tools such as Apache NiFi for orchestration, ensuring that data was cleansed and enriched to meet analytical requirements. I also focused on ETL's automation to enhance efficiency, utilizing scripting languages like Python for automation tasks.

For database performance tuning, I typically start with analyzing query performance, utilizing query execution plans to identify bottlenecks. I optimize queries for efficiency and adjust database configurations for resource allocation. Additionally, I leverage indexes and ensure optimal data storage practices, coupled with regular maintenance schedules, to maximize performance.

In a previous role, we faced a significant data pipeline failure due to a scalability issue in our cloud environment. I conducted a thorough root cause analysis and identified the bottleneck in data ingestion. By re-architecting the pipeline to implement a more robust orchestration solution, I was able to significantly increase throughput and reliability, preventing future disruptions.

I ensure data security by implementing a multi-layer approach, starting with data encryption in transit and at rest. I incorporate access controls and regularly audit permissions to minimize exposure. Additionally, I stay updated with compliance policies and frameworks to align all data practices with regulatory standards for handling classified data.

I have substantial experience working with NoSQL databases such as MongoDB and Cassandra. I appreciate their flexibility for unstructured data and utilized them for projects requiring high availability and scalability. Understanding the trade-offs between consistency, availability, and partition tolerance helped me optimize their configurations for specific use cases.

Collaboration with cross-functional teams is vital. I routinely engage with SREs and Security Analysts during project phases to ensure that the data pipeline aligns with operational goals. Regular stand-ups and planning meetings help maintain transparency and enable me to address concerns early and integrate feedback from various stakeholders.

I prefer using Tableau and Power BI for data visualization due to their user-friendly interfaces and powerful analytics capabilities. These tools allow stakeholders to easily interpret complex datasets and derive actionable insights that contribute to data-driven decision-making across the organization.

Error management is crucial in data processing workflows. I implement automatic logging and alerting mechanisms to quickly identify and address errors. Analyzing error patterns enables me to enhance the pipeline design, minimizing future issues. I also document solutions and updates to ensure a smoother workflow going forward.

Mentoring is a significant aspect of my role as a Senior Data Engineer; I find it rewarding to share knowledge and guide junior engineers. I focus on fostering a supportive environment, encouraging questions, and providing hands-on training. By sharing my experiences and insights, I aim to elevate the team's overall expertise and encourage continuous learning.