Senior Cloud Data Engineer – Multi-Cloud Data Platforms

Role Overview

• Design, build, and maintain cloud-native data pipelines and data products across Azure and AWS using Databricks and Snowflake.
• Lead and contribute to the modernization and migration of on‑prem and legacy data platforms to cloud-based solutions.
• Implement batch and streaming data processing patterns using Spark and cloud-native services.
• Partner with data governance, security, and risk teams to ensure data products comply with enterprise governance, data privacy, and regulatory requirements.
• Enable secure data sharing and access patterns across domains and platforms using appropriate controls.
• Define and promote data engineering best practices, including CI/CD, testing, observability, performance tuning, and cost optimization.
• Collaborate with product owners and analytics teams to translate business requirements into well-modeled, high-quality datasets.
• Work closely with cloud and security architects to implement secure, scalable, and resilient data solutions.
• Support and mentor junior engineers through design reviews, code reviews, and technical guidance.

Requirements

• Bachelor’s degree, or equivalent work experience
• Three to five years of relevant experience
• 8+ years of experience in data engineering, with significant experience on cloud platforms.
• Proven hands-on experience building and operating data solutions in Azure and/or AWS.
• Strong experience delivering production-grade data pipelines and data products.
• Solid understanding of data governance, data quality, and security concepts in regulated environments.
• Excellent communication skills and ability to collaborate across engineering, product, and governance teams.
• Experience with data architecture and platform design in large enterprises.
• Strong hands-on experience with Azure Data Platform services, including: Azure Data Factory, Azure Data Lake Storage, Azure Synapse Analytics (or Fabric equivalent experience)
• Experience with AWS data services, such as AWS Glue, S3, and event-driven integrations.
• Deep experience with Databricks (Spark, Delta Lake, performance tuning).
• Strong working knowledge of Snowflake, including data modeling, ingestion patterns (e.g., Snowpipe), and data sharing.
• Expertise in Apache Spark for large-scale data processing.
• Experience building batch and near-real-time data pipelines.
• Strong SQL skills and experience with dimensional and analytical data modeling.
• Experience designing reusable, domain-oriented data products.
• Experience with API-based integrations (REST; familiarity with SOAP and GraphQL is a plus).
• Hands-on experience integrating with API gateways.
• Understanding of messaging and streaming platforms such as Kafka, MQ, AWS SQS, or RabbitMQ.
• Strong understanding of IAM, RBAC, OAuth 2.0, TLS/mTLS, and JWT.
• Experience implementing secure data access patterns in cloud environments.
• Familiarity with data cataloging, lineage, and metadata management concepts.
• Experience enabling self-service analytics and BI using tools such as Power BI, Tableau, or equivalent.
• Support AI initiatives through the data platform and data products.
• Prior experience in financial services or other highly regulated industries.
• Professional certifications in Microsoft Azure and/or AWS.
• Strong problem-solving skills and a track record of delivering scalable, efficient data solutions.
• Master’s degree in a relevant technical field.

Tech Stack

• Apache
• AWS
• Azure
• Cloud
• GraphQL
• Kafka
• RabbitMQ
• SOAP
• Spark
• SQL
• Tableau

Benefits

• Healthcare (medical, dental, vision)
• Basic term and optional term life insurance
• Short-term and long-term disability
• Pregnancy disability and parental leave
• 401(k) and employer-funded retirement plan
• Paid vacation (from two to five weeks depending on salary grade and tenure)
• Up to 11 paid holiday opportunities
• Adoption assistance
• Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law