Data Engineer, Assistant Vice President

State Street

. Design, build, and maintain scalable data pipelines using PySpark, Python, and Spark SQL .

Posted 10/1/2026full-timeUnited StatesLead💰 $100,000 - $167,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing scalable data pipelines using Databricks, PySpark, and SQL, while ensuring data quality and compliance with governance standards. Proficient in implementing lakehouse architecture and automating workflows for efficient data processing and analytics.

Highest-signal resume keywords

Databricks ExpertisePySpark ProficiencyETL/ELT FrameworksData Governance UnderstandingAWS Data Platform Services

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentSpark SQLDelta LakeData Modeling TechniquesPerformance OptimizationContainerization with DockerAPI IntegrationData Quality AssuranceBatch and Streaming WorkflowsData Ingestion Frameworks

Soft Skills

CollaborationProblem-SolvingLeadershipCommunicationAnalytical Thinking

Tools & Technologies

DatabricksAWS S3Power BIPower AppsAzure DevOpsHarnessDatabricks ReposUnity CatalogSQL ServerOracle

Industry Keywords

Data EngineeringComplianceFinancial ServicesData GovernanceAudit RequirementsLegal Data ConstructsMetadata ManagementSensitive Data HandlingAI/ML Use CasesUnstructured Data Processing

Tech Stack

Tools & technologies

ApacheAWSAzureCloudDockerETLOraclePySparkPythonSparkSQLUnity

About the role

Key responsibilities & impact

Design, build, and maintain scalable data pipelines using PySpark, Python, and Spark SQL
Develop and optimize ETL/ELT workflows on Databricks using Delta Lake
Implement Bronze/Silver/Gold Lakehouse architecture for enterprise data platforms
Build and manage Databricks Jobs, Workflows, and Notebooks for batch and streaming workloads
Develop reusable frameworks for data ingestion, processing, and orchestration
Containerize data workloads using Docker and automate processes via scripting
Integrate Databricks data pipelines with Power Apps and Power Automate
Enable data exposure for business users via APIs, connectors, and curated datasets
Design and optimize Databricks data lakehouse architectures
Integrate data from SQL Server, Oracle, and other enterprise source systems
Apply dimensional modeling, partitioning, and optimization techniques
Work with structured and semi-structured data such as JSON and Parquet
Tune performance using caching, indexing, and Spark optimization techniques
Publish curated datasets for Power BI dashboards, Power Apps, and Power Automate workflows
Ensure data quality through unit testing, validation frameworks, and automated checks
Monitor and troubleshoot distributed Spark workloads and pipelines
Analyze logs and resolve production issues across Databricks and cloud environments
Maintain data lineage, consistency, and audit readiness
Collaborate with Legal, Security, Compliance, and Enterprise Data teams
Translate business requirements into data engineering designs
Act as a Databricks and Lakehouse architecture subject matter expert
Lead initiatives with minimal supervision and own deliverables
Support data governance using Databricks Unity Catalog and AWS controls such as IAM and KMS
Ensure adherence to data privacy, regulatory, security, and audit requirements
Implement and maintain data access controls and data classification and handling standards
Collaborate with IAM and security teams to ensure secure data access
Design and maintain CI/CD pipelines using Harness, Azure DevOps, or GitHub
Automate deployment of Databricks assets using Databricks Repos and CLI
Monitor, schedule, and optimize workflows using Databricks orchestration tools
Maintain architecture, data flow, and runbook documentation
Continuously improve performance, scalability, and cost efficiency

Requirements

What you’ll need

Bachelor's or Master's degree in computer science, Data Engineering, Information Systems, or a related technical discipline
8+ years of experience in Data Engineering or data platform development
Strong hands-on experience with Databricks and Apache Spark
Proficiency in PySpark, Python, and SQL
Experience with AWS data platform services, including S3, Glue, Lambda, and IAM
Experience working with Delta Lake and lakehouse architecture
Solid understanding of distributed data processing
Solid understanding of ETL/ELT frameworks
Solid understanding of data modeling techniques
Hands-on experience with Databricks platform components, including Delta Lake, Workflows, and Unity Catalog
Strong experience building end-to-end batch and streaming data pipelines using AWS and Databricks
Familiarity with performance optimization techniques in Spark and Delta Lake
Experience supporting analytics, reporting, or AI/ML use cases on a lakehouse platform
Understanding of data governance, metadata management, and security controls
Experience in Legal, Compliance, Financial Services, or regulated industries
Understanding of legal data constructs such as contracts, clauses, obligations, and matters
Exposure to unstructured data processing or document/NLP pipelines
Experience with Power BI, Power Apps, or Power Platform
Experience handling sensitive data in audit-driven environments

Benefits

Comp & perks

Retirement savings plan (401K) with company match
Insurance coverage including basic life, medical, dental, vision, long-term disability, and other optional additional coverages
Paid time off including vacation, sick leave, short term disability, and family care responsibilities
Employee Assistance Program
Incentive compensation including eligibility for annual performance-based awards
Eligibility for certain tax advantaged savings plans
Inclusive development opportunities
Flexible work-life support
Paid volunteer days
Employee networks