Job : Sr. Data Engineer with Insurance Domain(W2) Location : Virginia, Reston Skills : Python, Jupyter Notebooks, Spark, PySpark, Spark SQL, Delta Lake

Sr. Data Engineer with Insurance Domain exp with 15+ years Remote GC/USC

Looking for VERY senior resources, up to hands-on lead level Experienced with Assertion based Architecture Engineers vs "coders" Coding is done in Jupyter Notebooks on Delta Lakes Need resources who can articulate design and build highly scalable solutions before jumping into coding Do NOT want resources who need to be told what to do Need critical thinkers who can troubleshoot and debug Independent workers, self starters, who speak up and raise impediments and offer solutions Ability to create technical documentation/designs/blueprints for repeatable development patterns

Required skills: Python Jupyter Notebooks Delta Lake Spark, PySpark, Spark SQL Serverless data infrastructure Data Vault 2.0 methodology experience Great Expectations data quality validation Automated Testing

Bonus skills: Kakfa streaming HUGE plus if they have solid background here Insurance background Experience leading mid-to sr. level engineers Apache Airflow Agentic AI implementations

Key Responsibilities: Design, develop, and maintain data pipelines using Python, PySpark, and Spark SQL to process and transform large-scale datasets. Implement Delta Lake architecture to ensure data reliability, consistency, and integrity for large, distributed datasets. Utilize serverless data infrastructure (e.g., AWS Lambda, Azure Functions, Databricks) to build scalable and cost-efficient data solutions. Collaborate with Data Scientists and Analysts by creating reusable Jupyter Notebooks for data exploration, analysis, and visualization. Optimize and manage data storage and retrieval processes, ensuring high performance and low latency. Implement best practices for data security, governance, and compliance within the data infrastructure. Work closely with cross-functional teams to understand data requirements and deliver solutions aligned with business objectives. Continuously monitor, troubleshoot, and improve the performance of data processing pipelines and infrastructure. Qualifications: 15+ years of experience in data engineering or related fields. Strong programming skills in Python with experience in data processing frameworks like PySpark. Extensive hands-on experience with Apache Spark and Spark SQL for processing and querying large datasets. Expertise with Delta Lakes for implementing scalable data lakehouse architectures. Experience with Jupyter Notebooks for prototyping and collaboration with data teams. Familiarity with serverless data technologies such as AWS Lambda, Azure Functions, or similar platforms. Proficient in working with cloud platforms such as AWS, Azure, or Google Cloud. Experience with data pipeline orchestration tools (e.g., Apache Airflow, Prefect, or similar). Solid understanding of data warehousing, ETL/ELT pipelines, and modern data architectures. Strong problem-solving skills and ability to work in a collaborative environment. Experience with CI/CD pipelines and DevOps practices is a plus.

Preferred Qualifications: Experience with Databricks for data engineering workflows. Familiarity with modern data governance practices and tools like Apache Atlas or AWS Glue. Knowledge of machine learning workflows and how data engineering supports AI/ML models