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Manager Data Analytics Engineer Jobs in Norfolk, VA

... manage bronze/silver/gold data modeling patterns within a Delta Lake or comparable lakehouse ... on analytics engineering best practices, data modeling standards, and technical approaches • ...

We are seeking an experienced Data Engineering Manager with deep technical expertise and the ... Make recommendations for enterprisewide data, data onboarding, and selfservice analytics roadmap ...

We are seeking an experienced Data Engineering Manager with deep technical expertise and the ... Make recommendations for enterprise-wide data, data onboarding, and self-service analytics roadmap ...

Data Analyst

Norfolk, VA · On-site

$22 - $25/hr

This role blends data analysis, variable data programming, and prepress production to ensure ... You will also serve as a key resource for our Customer Communications Management (CCM) platform ...

Data Engineer

Fort Eustis, VA · Hybrid

$108.40K - $130.10K/yr

... analytical purposes. * Experience managing CI/CD processes to include branching strategies ... Provide demonstrations on data engineering efforts to an audience of varying technical backgrounds.

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Manager Data Analytics Engineer information

See Norfolk, VA salary details

$43.1K

$125.5K

$171.7K

How much do manager data analytics engineer jobs pay per year?

As of May 28, 2026, the average yearly pay for manager data analytics engineer in Norfolk, VA is $125,506.00, according to ZipRecruiter salary data. Most workers in this role earn between $110,800.00 and $133,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Manager Data Analytics Engineer, and why are they important?

To thrive as a Manager Data Analytics Engineer, you need a strong background in data engineering, analytics, and leadership, typically with a degree in computer science or a related field. Familiarity with tools like SQL, Python, data warehousing platforms (e.g., Snowflake, Redshift), and certifications in cloud technologies or data management are common requirements. Excellent communication, problem-solving, and team management skills set top performers apart in this role. These competencies are essential for driving data strategy, ensuring data quality, and leading analytics teams to deliver actionable business insights.

How does a Manager Data Analytics Engineer typically balance technical project work with team leadership responsibilities?

As a Manager Data Analytics Engineer, you are expected to split your time between overseeing complex analytics engineering tasks and guiding your team’s development. This involves setting project priorities, conducting code reviews, and ensuring data solutions align with business goals, while also mentoring team members and facilitating collaboration with stakeholders like data scientists and business analysts. Successful managers often establish clear communication channels and delegate tasks effectively, so they can stay hands-on with key projects while supporting the professional growth of their team.

What is a Manager Data Analytics Engineer?

A Manager Data Analytics Engineer is a professional who leads a team of data analytics engineers responsible for designing, building, and maintaining data systems and analytics solutions. They oversee data pipeline development, ensure data quality, and collaborate with stakeholders to translate business requirements into technical solutions. In addition to technical expertise, they manage project timelines, mentor team members, and help drive data-driven decision-making across the organization.

What is the difference between Manager Data Analytics Engineer vs Data Analytics Engineer?

AspectManager Data Analytics EngineerData Analytics Engineer
Required CredentialsBachelor's or Master's in Data Science, Analytics, or related field; often leadership experienceBachelor's or Master's in Data Science, Analytics, or related field
Work EnvironmentLeads teams, manages projects, collaborates with stakeholdersDevelops data models, analyzes data, implements solutions
Employer & Industry UsageUsed in tech, finance, healthcare, and large enterprisesCommon in similar industries, often within data teams

The main difference is that a Manager Data Analytics Engineer oversees teams and projects, focusing on leadership and strategic planning, while a Data Analytics Engineer primarily develops and implements data solutions. Both roles require strong technical skills, but the manager role adds a layer of team management and stakeholder communication.

What are popular job titles related to Manager Data Analytics Engineer jobs in Norfolk, VA? For Manager Data Analytics Engineer jobs in Norfolk, VA, the most frequently searched job titles are:
What job categories do people searching Manager Data Analytics Engineer jobs in Norfolk, VA look for? The top searched job categories for Manager Data Analytics Engineer jobs in Norfolk, VA are:
What cities near Norfolk, VA are hiring for Manager Data Analytics Engineer jobs? Cities near Norfolk, VA with the most Manager Data Analytics Engineer job openings:
Lead Analytics Engineer

Lead Analytics Engineer

eShipping, LLC

Virginia Beach, VA • Remote

$80K - $115K/yr

Full-time

Posted 19 days ago


Job description

Position Summary

The Lead Analytics Engineer serves as the primary analytics resource embedded within the Solutions team, bridging the gap between complex data systems and business decision-making. This role combines deep technical expertise in analytics engineering with a consultative partnership approach — translating business needs into well-structured data models, building scalable data pipelines, and equipping cross-functional stakeholders with the insights and tools they need to drive outcomes. The Lead Analytics Engineer also provides technical mentorship and guidance to peers, reviewing work for accuracy, and helping elevate the team's overall data maturity.

Essential Duties and Responsibilities

Duties include but are not limited to the following:

• Design, build, and maintain scalable data models, reusable datasets, and analytics-ready assets that support reliable reporting, self-service analysis, and downstream decision-making across the organization

• Use SQL expertly to query, validate, and optimize data workflows, serving as a bridge between business questions, source systems, and scalable analytics solutions

• Write and maintain Python-based data transformation logic, including production-grade PySpark pipelines, to manipulate, validate, and operationalize complex datasets at scale

• Implement and manage bronze/silver/gold data modeling patterns within a Delta Lake or comparable lakehouse architecture

• Partner directly with the Solutions team as an embedded analytics resource, proactively identifying opportunities to leverage data for operational improvements

• Translate business requirements into technical specifications and deliver actionable insights to non-technical stakeholders

• Guide other team members on analytics engineering best practices, data modeling standards, and technical approaches

• Review the work of others to ensure data accuracy, consistency, and adherence to established standards

• Read and tune established reporting solutions to diagnose and resolve performance issues

• Collaborate with engineering, operations, finance, and customer success teams to understand evolving data needs

• Evaluate, learn, and adopt new tools, platforms, and frameworks quickly, helping the team stay effective in a fast-evolving data environment

Specific Department Responsibilities

• Serve as point of contact between data engineering function and Solutions team, fostering an embedded partnership

• Proactively identify gaps in existing data models and reporting and recommend improvements

• Contribute to the development and evolution of the organization's data strategy, including architecture decisions, tooling, and governance standards

• Support the evaluation and adoption of new data technologies and platforms

• Create and maintain technical documentation for data models, pipelines, and processes

• Participate in code reviews and provide constructive, growth-oriented feedback to peers

• Communicate project status, technical trade-offs, and data insights to both technical and non-technical audiences

Required Skills and Abilities

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skills, and/or ability required. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.

• Advanced proficiency in Python for data manipulation and analytics engineering, including writing clean, maintainable code to transform, validate, and operationalize complex datasets

• Expert-level proficiency in SQL, including window functions, CTEs, complex joins, MERGE operations, and query execution plan analysis, with the ability to use SQL as a bridge between business needs and scalable data solutions

• Familiarity with Delta Lake or comparable lakehouse technologies, including schema evolution, time travel, and medallion architecture patterns

• Demonstrated ability to quickly learn new tools, platforms, and frameworks, and become productive with emerging technologies in a fast-evolving data environment

• Demonstrated ability to translate complex business requirements into well-structured, scalable data models

• Excellent written and verbal communication skills, with ability to explain technical concepts to non-technical stakeholders

• Strong analytical and problem-solving skills with keen attention to detail

• Ability to work independently with minimal oversight while exercising sound judgment

• Comfortable mentoring others and providing technical guidance without direct management authority

• Ability to manage multiple priorities and adapt in a fast-paced environment

• Experience working with BI and visualization tools (e.g., Power BI, Apache Superset, or similar)

• Familiarity with cloud data platforms such as Apache Spark, Databricks, Azure Data Lake, or comparable environments

Minimum Education and Experience

• Bachelor's degree in Computer Science, Data Science, Information Systems, Statistics, or a related field — or equivalent practical experience

• 5+ years of professional experience in analytics engineering, data engineering, or a senior data analyst role with significant hands-on data modeling responsibilities

• Hands-on production experience with Apache Spark and PySpark

• Working experience with lakehouse architectures (Apache Spark or comparable)

• Track record of partnering directly with business teams (operations, finance, solutions, customer success, etc.) as a primary analytics resource

• Experience mentoring or guiding peers in a technical environment

• Freight, logistics, or transportation industry experience preferred

Physical Demands and Work Environment

The physical demands and work environment characteristics described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. This description reflects management’s assignment of essential functions; it does not proscribe or restrict the tasks that may be assigned.

• Physical Demands: While performing the duties of this job, the employee is regularly required to remain in a stationary position for at least 50% of the time. The employee needs to occasionally move about inside the office to access file cabinets, office machinery, etc. The general level of physical activity would be defined as sedentary. The employee is regularly required to operate a computer and other office productivity machinery, such as a calculator, telephone, copy machine, and printer. Some movements of the hands, arms, and wrists may involve repetitive motions. Specific vision abilities required by this job include the ability to detect, determine, perceive, identify, recognize, judge, observe, inspect, estimate, and assess various activities and surroundings.

• Cognitive/Mental Requirements: While performing the duties of this job, the employee is regularly required to comprehend and use basic language, either written or spoken, to communicate simple and complex information, ideas, and information. The employee is also required to use logic to define problems, collect information, establish facts, draw valid conclusions, interpret information, and deal with abstract variables for unique or unfamiliar situations. The employee must use problem-solving skills to formulate and apply appropriate courses of action for routine or familiar situations. The employee may be required to perform numerical operations including basic counting, adding, subtracting, multiplying, and dividing or more complex quantitative calculations.

• Work Environment: While performing the duties of this job, the employee is inside a central heat and air-conditioned office building. The noise level in the work environment is minimal.

Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of an employee. Duties, responsibilities, and activities may change at any time with or without notice.

eShipping is an Equal Opportunity Employer.