Lead DevOps Data Engineer

SS&C Technologies•Washington, DC

2d•Remote

About The Position

As a Lead DevOps Data Engineer at DST Health Solutions, LLC, you will play a pivotal role in designing, building, and maintaining our data infrastructure and pipelines, ensuring scalability, reliability, and security. You will lead a team of engineers, driving best practices in DevOps methodologies for data platforms and fostering a culture of continuous improvement and automation.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field; Master's degree preferred.
7+ years of experience in data engineering, with at least 3 years in a lead or senior role focusing on DevOps practices.
Proven expertise in designing and building large-scale data pipelines using ETL/ELT tools and techniques utilizing PostgreSQL.
Expertise in scripting languages such as Python or Java, and strong SQL skills.
Solid experience with Infrastructure as Code (IaC) tools like Terraform.
Demonstrated experience with CI/CD pipelines (e.g. Github actions, Airflow, PySpark) for data solutions.
Experience with containerization technologies (Docker, Kubernetes).
Strong understanding of data warehousing concepts, dimensional modeling, and data lake architectures.
Experience in tabular data (e.g. Hydra table)
Excellent leadership, communication, and interpersonal skills with the ability to mentor and guide technical teams.
Ability to work effectively in a fast-paced, collaborative, and agile environment.

Nice To Haves

Experience with real-time data processing frameworks (e.g., Kafka, Spark Streaming) is a plus.

Responsibilities

Lead the design, development, and implementation of robust, scalable, and secure data pipelines and data warehouses using cloud-native services and PostgreSQL.
Drive the adoption and implementation of DevOps principles and practices (CI/CD, infrastructure as code, automated testing, monitoring) across data engineering initiatives.
Mentor and guide a team of data engineers, fostering their technical growth and ensuring adherence to engineering best practices and architectural standards.
Collaborate with data scientists, analysts, and other engineering teams to understand data requirements and translate them into efficient and effective data solutions.
Help teams to optimize and maintain queries essential in data pipelines, reporting, and analytics.
Troubleshoot and resolve complex data-related issues, optimizing performance and ensuring data integrity.
Evaluate and recommend new technologies and tools to enhance our data platform capabilities and operational efficiency.
Develop and maintain comprehensive documentation for data architecture, pipelines, and operational procedures.
Participate in on-call rotations as needed to support critical data infrastructure.