Senior Data Engineer

About the role 

We are seeking an accomplished Senior Data Engineer to join our dynamic and rapidly expanding Tech Ops division. With significant projected growth, this is an opportunity to drive meaningful technical impact. In this role, you will work with internal engineering teams to design, implement, and optimize complex data integration solutions within a modern, large‑scale cloud environment. 

You will leverage advanced skills in distributed computing, data architecture, and cloud-native engineering to enable scalable, resilient, and high‑performance data ingestion and transformation pipelines. As a trusted technical advisor, you will ensure high-quality, compliant data operations across the lifecycle. 

Your day to day 

• Architect, design, and implement high-volume batch and real-time data pipelines using PySpark, SparkSQL, Databricks Workflows, and distributed processing frameworks. 

• Build end‑to‑end ingestion frameworks integrating with Databricks, Snowflake, AWS services (S3, SQS, Lambda), and vendor data APIs, ensuring data quality, lineage, and schema evolution. 

• Develop data modeling frameworks, including star/snowflake schemas and optimization techniques for analytical workloads on cloud data warehouses. 

• Translate complex business requirements into detailed technical specifications, engineering artifacts, and reusable components. 

• Establish and enforce data engineering best practices, such as CI/CD for data pipelines, code versioning, automated testing, orchestration, logging, and observability patterns. 

• Conduct performance profiling and optimize compute costs, cluster configurations, partitions, indexing, and caching strategies across Databricks and Snowflake environments. 

• Produce high-quality technical documentation including runbooks, architecture diagrams, and operational standards. 

• Participate in planning, design discussions, technical reviews, and continuous improvement activities as part of an iterative development lifecycle. 

• Proactively monitor, troubleshoot, and support production data pipelines to ensure reliability, performance, and timely data availability. 

• Identify root causes of data pipeline issues and implement corrective and preventive actions to improve operational stability and data quality. 

• Mentor junior engineers through technical reviews, coaching, and training sessions for both internal teams and clients. 

What you bring to the team 

• Bachelor’s degree in Computer Science, Computer Engineering, or a closely related technical field. 

• 5+ years of hands‑on experience as a Data Engineer working with large‑scale, distributed data processing systems in modern cloud environments. 

• Working knowledge of U.S. healthcare data domains—including claims, eligibility, and provider datasets—and experience applying this knowledge to complex ingestion and transformation workflows. 

• Strong ability to communicate complex technical concepts clearly across both technical and non‑technical stakeholders. 

• Expert‑level proficiency in Python, SQL, and PySpark, including developing distributed data transformations and performance‑optimized queries. 

• Demonstrated experience designing, building, and operating production‑grade ETL/ELT pipelines using Databricks, Airflow, or similar orchestration and workflow automation tools. 

• Proven experience architecting or operating large‑scale data platforms using dbt, Kafka, Delta Lake, and event‑driven/streaming architectures, within a cloud‑native data services or platform engineering environment—requiring specialized knowledge of distributed systems, scalable data pipelines, and cloud‑scale data processing. 

• Experience working with structured and semi‑structured data formats such as Parquet, ORC, JSON, and Avro, including schema evolution and optimization techniques. 

• Strong working knowledge of AWS data ecosystem components—including S3, SQS, Lambda, Glue, IAM—or equivalent cloud technologies supporting high‑volume data engineering workloads. 

• Proficiency with Terraform, infrastructure‑as‑code methodologies, and modern CI/CD pipelines (e.g., GitLab) supporting automated deployment and versioning of data systems. 

• Deep expertise in SQL and compute optimization strategies, including Z‑Ordering, clustering, partitioning, pruning, and caching for large‑scale analytical and operational workloads. 

• Hands‑on experience with major cloud data warehouse platforms such as Snowflake (preferred), BigQuery, or Redshift, including performance tuning and data modeling for analytical environments. 

What we would like to see but not required: 

• Experience in large-scale healthcare or payer data environments. 

What you’ll get in return 

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Working Arrangements

• Standard hours: 9 hours/day, 5 working days
• Location: Onsite
• Shift: 10 AM – 7 PM local time