Principal Data Engineer

Metropolitan Council•390 Robert St. N St. Paul, MN

13d•Hybrid

About The Position

The Principal Data Engineer works with business and IS leaders and stakeholders to establish data engineering strategies that support the organization’s goals and objectives. They lead and influence decisions around data architecture, data processing, and data governance, collaborating with IS and business units to create solutions that make data available to systems and/or data consumers, such as system users, developers, data scientists, or business analysts. They create and socialize the data engineering technology roadmap and plan how to get there. They evaluate new technologies, tools, and techniques, and work closely with the IS architects to build data engineering solutions on top of the overall technology architecture. They help establish data governance policies, procedures, standards, and best practices to ensure compliance with industry regulations, security and data privacy laws, and information security requirements. This role designs, develops, and maintains data solutions. This includes databases (DBs), data lakes, data warehouses, data marts, and data movement (data pipelines/ETL/ELT), as well as integrating analytics and data science outputs into existing business processes or systems. They administer, monitor, and maintain data solutions and tools, ensuring their security, performance, and availability. They also identify and resolve data quality issues. The Principal Data Engineer requires the highest degree of expertise in database and data platform technologies, data modeling, structured and unstructured data, and strong programming skills. They often act as technical leads and work on initiatives ranging from small to large, advising and mentoring others. They play a critical role in ensuring the data is properly managed and leveraged to support strategic decision-making and to achieve business objectives.

Requirements

Master's degree and four (4) years of experience
Bachelor’s degree and six (6) years of experience
Associate’s degree and eight (8) years of experience
High school diploma/GED and ten (10) years of experience
Developed data strategies & roadmaps
Defined data standards, best practices, policies, & procedures
Developed data solutions for operations, reporting, & analytics
ITIL, Incident, Service, Change & Problem Management experience
Knowledge of & experience with data privacy laws, security, & industry regulations
Writing & tuning complex SQL queries
Extensive Experience with Databricks, Snowflake, or equivalent

Nice To Haves

Experience with Azure, AWS, GCP, or other cloud service architecture
Experience working with CI/CD pipelines via GitHub or equivalent source control platform
Experience with Terraform or other Infrastructure as Code frameworks
Working knowledge of Spark or equivalent distributed computing engine
Experience as a technical lead on a team
Modern cloud data platform experience in Databricks or equivalent
Developing & supporting data integrations in Python or Scala
Reporting & data visualization experience
Knowledge of IS teams, roles & responsibilities, services, & general technologies.
Understand the data management team, group functions, & data technologies.
Knowledge of the various business units & the types of data each has and needs.
Understand & use incident, service, problem, & change management processes.
Strong interpersonal relationships within and across teams, to remove barriers, reduce risk, build trust and improve insights.
Skill and comfort in delivering strategic guidance to IS teams and leadership.

Responsibilities

Partners with executives, architects, and technical leaders.
Guide to multi-team initiatives.
Represent data engineering in strategic planning.
Lead the design and architecture of enterprise data platforms.
Define engineering standards, frameworks, and best practices.
Drive modernization of data systems and adoption of cloud-native solutions.
Solve highly complex data engineering challenges across platforms.
Partner with leadership to define long-term data strategy.
Provide expert-level mentoring and oversight to engineering teams.
Develop data engineering strategies for operations, reporting, and analytics.
Create and socialize the data engineering roadmap to achieve the strategic plan.
Evaluate new technologies, tools, and techniques and make recommendations.
Develop data policies, procedures, standards, and best practices.
Mentor, influence, and advise data engineers on industry best practices.
May act as a technical lead on small to large data-related initiatives.
Oversee performance, reliability, and security of enterprise data systems.
Guide database and platform engineering teams.
Set and enforce access/security standards.
Define data governance and quality frameworks.
Ensure compliance across platforms.
Oversee classification, retention, and audit practices.
Lead root-cause analysis for major incidents.
Direct capacity planning and optimization.
Set operational standards and reliability targets.
Ensure data systems are available, scalable, secure, and supportable.
Administer, monitor, and support 24x7x365 operations, ensuring data security, performance, and availability.
Resolve incidents and meet Service Level Agreements (SLAs).
Ensure compliance with industry regulations, security, and data privacy laws.
Recommend performance improvements, optimizations, and automation.
Help fulfill data practices requests, complete audits, and respond to cybersecurity events.
Efficiently complete data service requests.
Identify and resolve data quality issues.
Define technical architecture for Council-wide data solutions.
Design highly scalable pipelines and data models.
Guide to the adoption of new technologies.
Review and approve complex engineering designs.
Design, develop, and implement data solutions for operational, reporting, and analytical items.
Create solutions that make data available to systems and/or data consumers, including system users, developers, data scientists, and business analysts.
Build and leverage CI/CD development practices.
Integrate analytics and data outputs into business processes or systems.
Effectively collaborate with business teams on data needs and requirements.
Partner with IS teams to deliver available, secure, and supportable solutions for on-premises, hybrid, and cloud environments.
Mentor and train IS and business teams on data solutions.