Senior Data Engineer

Role Overview
We are seeking a hands-on Data Engineer to join a highly technical, engineering-focused team. This role is ideal for someone who thrives in building robust, scalable data pipelines, enjoys solving complex data problems, and partners closely with product and engineering teams to deliver high-impact solutions.
You will work primarily in Google Cloud Platform (GCP), leveraging SQL, Python, and Spark to design, build, and optimize data pipelines supporting enterprise-scale data needs.
Key Responsibilities
Data Engineering & Pipeline Development
Design, build, and maintain scalable, reliable data pipelines and ETL/ELT processes.
Develop and optimize data workflows within Google Cloud Platform (GCP).
Ensure data quality, performance, and reliability across ingestion, transformation, and delivery layers.
Support API-driven integrations and data movement across systems.
Coding & Technical Execution
Write clean, efficient, production-ready code primarily in Python.
Develop complex SQL queries, stored procedures, and data models.
Work with Spark to process and transform large datasets.
Build and manage DAGs and workflows for orchestration.
Quality, Validation & Monitoring
Implement best practices for testing, validating, and monitoring data pipelines.
Perform data exploration, profiling, and validation to ensure accuracy and integrity.
Troubleshoot data issues and drive root cause analysis.
Participate in a live, proctored technical screening (SQL + Python) as part of the interview process.
Collaboration & Agile Delivery
Partner closely with software engineers, product owners, and Aetna technology counterparts.
Operate in a highly hands-on, engineering-heavy agile environment.
Participate in sprint ceremonies, backlog refinement, and technical design discussions.
Work independently while collaborating effectively with cross-functional teams.
Required Skills & Experience
Technical Skills
3+ years of relevant experience in Data Engineering / ETL Development.
Strong SQL expertise, including complex queries, data analysis, profiling, validation, and performance tuning.
3+ years of hands-on SQL experience working with large datasets.
1+ year of experience with Python, Spark, or Java for data engineering and pipeline development.
Experience leveraging cloud technologies for data engineering and analytics
GCP preferred (AWS or Azure acceptable).
General ETL development experience, including workflow orchestration (DAGs).
Professional Experience
3–5+ years of experience as a Data Engineer or in a similar role.
Experience working in enterprise-scale environments with large, complex datasets.
Proven ability to operate independently while collaborating across technical and non-technical teams.
Nice-to-Have Qualifications
Healthcare or insurance industry experience (helpful but not required).
Experience with:
Data modeling
Orchestration tools
Distributed systems
Advanced Spark use cases
Ideal Candidate Profile
You are a hands-on data engineer who values technical depth and craftsmanship. You enjoy building and optimizing data pipelines, digging into data quality issues, and partnering closely with engineering and product teams. You are fluent in SQL and Python, comfortable working in GCP, and ready to contribute immediately in a fast-paced, engineering-driven environment.