Senior Data Engineer – Data Platform

SunStrong Management

Senior Data Engineer building and operating foundational data infrastructure for analytics at SunStrong Management. Collaborates with teams to deliver production-grade solutions with quality controls and security.

Posted 7/11/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

AirflowApacheAWSAzureCloudDockerGoogle Cloud PlatformKubernetesPostgresPythonSQLTerraform

About the role

Key responsibilities & impact

Design, build, and operate end-to-end data pipelines (batch and near-real-time) that ingest, transform, and deliver data from diverse sources into the enterprise data platform.
Develop and maintain curated data models, marts, and shared datasets in Snowflake and PostgreSQL that meet performance, quality, and access-control requirements for multiple internal customers.
Implement data quality frameworks including automated validation, schema enforcement, reconciliation checks, duplicate detection, and exception reporting with clear audit trails.
Partner with domain teams (e.g., Asset Management, Finance, Operations) to understand data needs, define contracts and SLAs, and deliver platform capabilities that reduce bespoke engineering and manual effort.
Build parameterized, reusable pipeline components and templates that standardize ingestion patterns, transformations, and deployment across the platform.
Establish and maintain data lineage, metadata, and documentation so stakeholders can trace data from source to consumption with confidence.
Collaborate with IT and security to implement role-based access controls, data masking, encryption, and compliance requirements across platform resources.
Own pipeline orchestration, scheduling, dependency management, and alerting using workflow tools (e.g., Airflow) to ensure reliable, recoverable execution.
Improve platform observability through logging, metrics, SLA monitoring, and incident response practices that minimize downtime and data freshness gaps.
Support CI/CD and infrastructure-as-code practices for data platform assets, including version control, automated testing, and safe promotion across environments.
Evaluate and integrate new platform technologies and patterns (e.g., streaming, CDC, data mesh principles) where they improve scalability, cost efficiency, or time-to-value.
Mentor junior engineers and contribute to platform standards, code review practices, and technical design documentation.

Requirements

What you’ll need

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field.
5+ years of experience in data engineering or platform engineering, preferably in a financial services or regulated industry (e.g., asset management, banking, insurance, fintech).
Strong SQL and Python skills, with a track record of building production-quality data pipelines, transformations, and validation frameworks.
Proficient at using AI-assisted development tools to design, build, and iterate on data pipelines while maintaining code quality, security, and governance standards.
Hands-on experience with Snowflake and PostgreSQL, including performance tuning, cost optimization, and secure multi-tenant data access patterns.
Experience with pipeline orchestration and workflow management tools (e.g., Apache, Airflow, Dagster, or equivalent).
Proficiency with Git, code review, and CI/CD practices for data platform development.
Experience designing dimensional or domain-oriented data models and delivering curated datasets for analytics and operational use cases.
Familiarity with data quality, lineage, and governance tooling and practices (preferred).
Experience with cloud data services (e.g., AWS, Azure, or GCP) and infrastructure-as-code (e.g., Terraform) is strongly preferred.
Exposure to streaming or change-data-capture (CDC) patterns and event-driven architectures is a plus.
Understanding of financial data domains (e.g., portfolio, investor reporting, accounting) is helpful but not required; curiosity and ability to partner with domain experts is essential.
Strong communication and collaboration skills; ability to translate ambiguous requirements into well-scoped technical designs and clear status reporting.
Familiarity with containerization (e.g., Docker/Kubernetes) and API/integration patterns for data services is a plus.

Benefits

Comp & perks

Health insurance
Retirement plans
Paid time off
Flexible work arrangements
Professional development

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Hard Skills & Tools

Data EngineeringData ModelingData Quality FrameworksAutomated TestingPerformance TuningChange Data CaptureInfrastructure-as-CodeVersion ControlData GovernanceDimensional Modeling

Soft Skills

CommunicationCollaborationMentoringProblem-SolvingCuriosity