Senior Data Engineer

About the Role

Key Responsibilities

• Design and develop robust, scalable ETL/ELT pipelines using PySpark and GCP services such as Data proc, Cloud Composer, Big Query, Cloud Storage, GKE etc.
• Develop integrations with other systems and applications using APIs, micro services, and service-oriented approaches.
• Implement end-to-end pipelines for drug pricing management activities while achieving operational efficiency.
• Build automated CI/CD pipeline using Cloud Build, GitHub Actions, or Jenkins for deploying data pipeline code and workflows.
• Lead architectural decisions and development for analytical applications.
• Set up observability using Cloud Monitoring, Cloud Logging, and Error Reporting to ensure pipeline reliability.
• Mentor junior engineers on data engineering best practices and promote best practices for code quality, version control, cost optimization, and data security in a GCP environment.

Requirements

• 3+ years of experience with SQL, NoSQL.
• 3+ years of experience with Python (or a comparable scripting language).
• 3+ years of experience with Data warehouses (such as data modeling and technical architectures) and infrastructure components.
• 3+ years of experience with ETL/ELT, and building high-volume data pipelines.
• 3+ years of experience with reporting/analytic tools.
• 3+ years of experience with Query optimization, data structures, transformation, metadata, dependency, and workload management.
• 3+ years of experience with Big data and cloud architecture.
• 3+ years of hands-on experience building modern data pipelines within a major cloud platform (GCP, AWS, Azure).
• 3+ years of experience with deployment/scaling of apps on containerized environment (i.e., Kubernetes, AKS).
• 3+ years of experience with real-time and streaming technology (i.e., Azure Event Hubs, Azure Functions, Kafka, Spark Streaming).
• 1+ year of soliciting complex requirements and managing relationships with key stakeholders.
• 1+ year of experience independently managing deliverables.

Nice to Have

• Experience in designing and building data engineering solutions in cloud environments (preferably GCP).
• Experience with Git, CI/CD pipeline, and other DevOps principles/best practices.
• Experience with bash shell scripts, UNIX utilities & UNIX Commands.
• Understanding of software development methodologies including waterfall and agile.
• Ability to leverage multiple tools and programming languages to analyze and manipulate data sets from disparate data sources.
• Knowledge of API development.
• Experience with complex systems and solving challenging analytical problems.
• Strong collaboration and communication skills within and across teams.
• Knowledge of data visualization and reporting.
• Experience with schema design and dimensional data modeling.
• Google Professional Data Engineer Certification.
• Knowledge of microservices and SOA.
• Formal SAFe and/or agile experience.
• Previous healthcare experience and domain knowledge.
• Experience designing, building, and maintaining data processing systems.
• Experience architecting and building data warehouse and data lakes.

Qualifications

• Bachelor's Degree or equivalent work experience in Computer Science, Information Systems, Data Engineering, Data Analytics, Machine Learning, or related field required.
• Master's Degree preferred.

Benefits & Perks

• Affordable medical plan options, a 401(k) plan (including matching company contributions), and an employee stock purchase plan.
• No-cost programs for all colleagues including wellness screenings, tobacco cessation and weight management programs, confidential counseling and financial coaching.
• Benefit solutions that address the different needs and preferences of our colleagues including paid time off, flexible work schedules, family leave, dependent care resources, colleague assistance programs, tuition assistance, retiree medical access and many other benefits depending on eligibility.