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
Six to eight years of relevant experience
Experience with data architecture and platform design in large enterprises.
Knowledge of data sharing, data mesh, or domain-driven data architecture concepts.
Strong problem-solving skills and a track record of delivering scalable, efficient data solutions.
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.
Master’s degree in a relevant technical field.

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data architecturedata platform designdata sharingdomain-driven data architectureAzure Data FactoryAzure Data Lake StorageAzure Synapse AnalyticsAWS GlueDatabricksApache Spark

Soft Skills

problem-solvingmentoringcollaborationcommunicationleadership

Certifications

Microsoft Azure certificationAWS certificationBachelor's degreeMaster's degree