Data Platform Engineer III

Finance of America•,

7d•$135,000 - $160,000

About The Position

Responsible for designing, developing, implementing, and supporting scalable cloud-based data platforms, pipelines, integrations, and lakehouse solutions that enable enterprise reporting, analytics, operational insights, and AI-driven initiatives. Extracts, transforms, and optimizes data from complex and diverse data sources while ensuring reliability, performance, governance, and operational efficiency across the enterprise data ecosystem.

Requirements

Minimum 7 years of related experience designing, developing, and supporting enterprise data warehouses, lakehouses, or modern cloud-based data platforms, preferably within the financial services industry.
Minimum 7 years of experience developing and supporting cloud-based data integration solutions such as Azure Data Factory, Microsoft Fabric Data Pipelines, Synapse Pipelines, or equivalent technologies.
Strong hands-on experience with Microsoft Fabric including Lakehouse, Warehouse, Data Pipelines, Notebooks, OneLake, semantic models, and Power BI integration.
Experience implementing medallion architecture and modern lakehouse design patterns.
Strong experience with SQL, Python, PySpark, notebooks, and distributed data processing technologies.
Experience with Azure services such as Azure Data Lake Storage, Azure SQL Database, Azure Functions, Logic Apps, Azure Key Vault, and Azure Monitor.
Experience with AWS services such as Amazon S3, AWS Lambda, Amazon RDS, CloudWatch, EC2, and IAM.
Strong understanding of dimensional modeling, semantic modeling, star schemas, data warehousing concepts, and lakehouse architecture principles.
Experience developing scalable ETL/ELT pipelines, orchestration frameworks, and reusable data engineering solutions.
Experience supporting Power BI environments including gateways, semantic models, refresh optimization, Direct Lake, and enterprise reporting integrations.
Understanding of data governance, data lineage, metadata management, data security, and cloud platform best practices.
Experience implementing monitoring, logging, observability, and operational reporting solutions for enterprise data platforms.
Understanding of cloud infrastructure, networking, identity and access management, security controls, and cost optimization principles.
Experience supporting AI, machine learning, generative AI, or advanced analytics initiatives through the delivery of trusted datasets.
Strong analytical thinking, troubleshooting, and problem-solving skills.
Experience working within Agile delivery environments.
Ability to manage multiple concurrent priorities and deliver high-quality solutions.
Strong verbal, written, and interpersonal communication skills.
Demonstrated ability to mentor junior engineers and collaborate effectively across technical and business teams.
Bachelor's Degree or comparable qualifications
Computer Science, Engineering, or related field.

Responsibilities

Designs and develops data pipelines to ingest, transform, and load data from various sources into the data ecosystem.
Designs, develops, and maintains scalable, secure, and reliable enterprise data platforms, pipelines, integrations, and cloud-native data solutions.
Designs and develops data pipelines to ingest, transform, validate, and load structured and unstructured data into enterprise lakehouse and warehouse environments.
Implements and supports modern data platform architectures including medallion, lakehouse, dimensional, and semantic modeling patterns.
Develops and maintains notebooks, data engineering frameworks, orchestration solutions, and reusable components using SQL, Python, PySpark, and cloud-native technologies.
Supports Microsoft Fabric environments including Lakehouse, Warehouse, Data Pipelines, Notebooks, OneLake, semantic models, and Power BI integration.
Supports enterprise Power BI solutions including gateways, refresh optimization, data connectivity, semantic models, and performance tuning.
Designs and implements integration patterns including APIs, event-driven integrations, cloud-native integrations, and file-based data movement processes.
Creates and maintains monitoring, logging, alerting, observability, and operational reporting frameworks to ensure platform reliability and SLA adherence.
Supports cloud infrastructure and modernization initiatives across Azure, AWS, Microsoft Fabric, and related cloud services.
Monitors platform health, compute utilization, refresh performance, storage efficiency, reliability, and operational metrics across data environments.
Supports AI, machine learning, predictive analytics, and enterprise reporting initiatives through the delivery of trusted, governed, and optimized datasets.
Collaborates with Analytics Engineers, Data Owners, Infrastructure teams, Security teams, and business stakeholders to understand requirements and improve platform efficiencies through automation and optimization.
Troubleshoots and resolves pipeline, infrastructure, integration, data quality, and performance issues across enterprise data platforms.
Contributes to platform standards, governance practices, documentation, and operational procedures.
Implements and supports CI/CD processes, source control standards, deployment automation, and environment management practices.
Researches emerging technologies, tools, and industry trends to recommend improvements to the enterprise data platform ecosystem.
Provides mentorship and technical guidance to junior engineers and team members.
Maintains a customer-first mentality while collaborating with stakeholders, leadership, and engineering teams.
Performs other duties as assigned.