Senior Data Engineer - Data Platform

SunStrong Management; United States; Presencial; USD 140.000 - 190.000; Jornada completa

Descripción de la vacante

SunStrong Management is seeking a Senior Data Engineer — Data Platform to design, build, and operate scalable data pipelines and models that power analytics and operations. You will partner with IT, Finance, and Asset Management teams to ensure reliable, governed data with strong quality controls and observability.

The role emphasizes data ingestion, modeling, platform reliability, and developer experience, including governance, security, and scalable architecture; prior financial services

Formación

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.
• 5+ years of experience in data engineering or platform engineering in financial services or regulated industries.
• Strong SQL and Python skills with production-quality data pipelines.
• Experience with Snowflake and PostgreSQL, including performance tuning and secure multi-tenant access.
• Experience with pipeline orchestration and workflow tools (Airflow, Dagster, etc.).
• Proficiency with Git, code review, and CI/CD practices for data platforms.
• Experience designing dimensional or domain-oriented data models and delivering curated datasets.
• Familiarity with data quality, lineage, governance tooling, and cloud data services (AWS/Azure/GCP).
• Exposure to streaming or CDC patterns and event-driven architectures is a plus.
• Strong communication and collaboration skills; ability to translate requirements into designs.
• Familiarity with Docker/Kubernetes and API/integration patterns is a plus.

Responsabilidades

• Design, build, and operate end-to-end data pipelines (batch and near real-time) for enterprise data platform.
• Develop and maintain curated data models and shared datasets in Snowflake and PostgreSQL.
• Implement data quality frameworks with automated validation and audit trails.
• Collaborate with domain teams to define contracts and SLAs and deliver platform capabilities.
• Build reusable pipeline components and templates for ingestion, transformations, and deployment.
• Establish data lineage, metadata, and documentation across data assets.
• Collaborate with IT and security for RBAC, data masking, and encryption.
• Own pipeline orchestration, scheduling, dependencies, and alerting (Airflow, Dagster).
• Improve platform observability with logging, metrics, and incident response.
• Support CI/CD and infrastructure-as-code for data platform assets.
• Evaluate new platform technologies (streaming, CDC, data mesh) to improve scale and value.
• Mentor junior engineers and contribute to standards and design docs.

Conocimientos

SQL & Python

Communication

Collaborative skills

CI/CD practices

Data modeling

Educación

Bachelor's or Master's in CS/Engineering/Data Science

Herramientas

Snowflake

PostgreSQL

Airflow

Dagster

Terraform

Docker

Kubernetes

Descripción del empleo

Job Summary

The Senior Data Engineer — Data Platform builds and operates the foundational data

Job Summary

The Senior Data Engineer — Data Platform builds and operates the foundational data infrastructure that powers analytics, reporting, and operational workflows across the organization. This role designs, develops, and maintains scalable batch and streaming pipelines, curated data models, and platform services that enable internal teams—including Asset Management, Finance, and Operations—to access reliable, well-governed data. The position partners with Data Engineering, IT, and business stakeholders to translate platform requirements into production-grade solutions with strong quality controls, observability, and security. Core emphasis areas include: (1) data ingestion and integration—building robust connectors and pipelines to land data from internal and third-party sources into the platform with clear lineage and auditability; (2) data modeling and curation—designing dimensional and domain-oriented models in Snowflake and PostgreSQL that support self-service analytics and downstream applications; and (3) platform reliability and developer experience—establishing standards, reusable frameworks, orchestration patterns, and monitoring to accelerate delivery while maintaining operational excellence.

Responsibilities

• 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.

Qualifications

• 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.