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Manager Data Analytics Engineer Jobs in Rochester, NY

This leader is responsible for the day-to-day management and supervision of Quality Engineers and Data Analysts and is also accountable for developing talent, strengthening scientific and compliance ...

Quality Data & Analytics Engineer

Fairport, NY

$68.40K - $88.30K/yr

The Division Quality Data & Analytics Engineer bridges industrial engineering, manufacturing, and ... Design and manage Power Platform solutions-including Power BI dashboards, PowerApps, and automated ...

Quality Data & Analytics Engineer

Fairport, NY · On-site

$68.40K - $88.30K/yr

The Division Quality Data & Analytics Engineer bridges industrial engineering, manufacturing, and ... Design and manage Power Platform solutions-including Power BI dashboards, PowerApps, and automated ...

Quality Data & Analytics Engineer

Fairport, NY

$68.40K - $88.30K/yr

The Division Quality Data & Analytics Engineer bridges industrial engineering, manufacturing, and ... Design and manage Power Platform solutions--including Power BI dashboards, PowerApps, and automated ...

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and ...

... Analytics / Solutions Architect - Azure Data Engineer / Azure Solutions Architect - Google Professional Data Engineer - DAMA CDMP (Certified Data Management Professional) - Informatica Certified ...

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Senior Associate & Summary At PwC, our people in data and analytics engineering focus on leveraging advanced ...

Data Governance- Manager

Rochester, NY · On-site

$99K - $232K/yr

... Analytics / Solutions Architect - Azure Data Engineer / Azure Solutions Architect - Google Professional Data Engineer - DAMA CDMP (Certified Data Management Professional) - Informatica Certified ...

... Management Studio (SSMS), Oracle GoldenGate, DBeaver, etc. • Experience with Tableau Prep, Alteryx, or similar data preparation tools. • Experience using Python or R for analytics or automation ...

... Analytics / Solutions Architect - Azure Data Engineer / Azure Solutions Architect - Google Professional Data Engineer - DAMA CDMP (Certified Data Management Professional) - Informatica Certified ...

Data Engineer

Rochester, NY · On-site

$99.60K - $156.50K/yr

Overview Source, extract, and manipulate large data sets to serve Business Analytics, Machine ... Management - Preferred * Operations - Preferred * Data Engineering - Preferred * Python ...

Data Engineer

Rochester, NY · On-site

$99.60K - $156.50K/yr

Overview Source, extract, and manipulate large data sets to serve Business Analytics, Machine ... Management - Preferred * Operations - Preferred * Data Engineering - Preferred * Python ...

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and ...

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Manager Data Analytics Engineer information

See Rochester, NY salary details

$43.9K

$128K

$175.1K

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 Rochester, NY is $127,987.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,000.00 and $135,700.00 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 the most commonly searched types of Data Analytics Engineer jobs in Rochester, NY? The most popular types of Data Analytics Engineer jobs in Rochester, NY are:
What are popular job titles related to Manager Data Analytics Engineer jobs in Rochester, NY? For Manager Data Analytics Engineer jobs in Rochester, NY, the most frequently searched job titles are:
What job categories do people searching Manager Data Analytics Engineer jobs in Rochester, NY look for? The top searched job categories for Manager Data Analytics Engineer jobs in Rochester, NY are:
What cities near Rochester, NY are hiring for Manager Data Analytics Engineer jobs? Cities near Rochester, NY with the most Manager Data Analytics Engineer job openings:
Quality Data & Analytics Engineer

Quality Data & Analytics Engineer

Corning Incorporated

Fairport, NY • On-site

$108.70K - $130.50K/yr

Full-time

Posted 6 days ago


Corning rating

8.2

Company rating: 8.2 out of 10

Based on 122 frontline employees who took The Breakroom Quiz

80th of 511 rated manufacturers


Job description

Job Summary:
Corning Incorporated is a leading innovator in glass, ceramic, and materials science. They are seeking a Quality Data & Analytics Engineer to bridge industrial engineering, manufacturing, and data systems, transforming fragmented data into actionable insights to improve quality and operations.
Responsibilities:
• Design and manage Power Platform solutions—including Power BI dashboards, PowerApps, and automated workflows—to support QMS and PEX operations.
• Translate complex manufacturing data into clear visualizations for all stakeholders using SalesForce, ETQ, SAP, and MES.
• Identify, assess, and close data gaps — proactively evaluate existing data sources across the quality, identify where critical data is missing, poorly structured, or uncaptured, and engineer new data collection methods, pipelines, and integrations to fill those gaps
• Use an industrial engineering perspective in data analysis by drawing on expertise in manufacturing processes, process control, OEE, yield analysis, cycle time, and reducing variation. This approach helps ensure that analytics results are practical and lead to real improvements in operations.
• Conduct comprehensive data analyses supporting continuous improvement initiatives, collaborating with process engineering and quality teams to identify root causes, track performance trends, and validate improvement outcomes
• Facilitate the integration of quality and production processes within ERP and MES systems to enhance system functionality, streamline data workflows, and improve overall manufacturing performance
• Champion the adoption and adherence to QMS and CI best practices across sites, serving as a subject matter expert (SME) for data-driven decision-making, process optimization, and analytical best practices
• Leverage artificial intelligence (AI) and emerging technologies to innovate, solve complex quality and operational challenges, and continuously advance data accuracy and efficiency across the division
• Lead projects for implementing new QMS, analytics, and continuous improvement software, ensuring seamless integration with existing systems and alignment with evolving business needs — from requirements gathering through go-live and sustainment
• Proactively identify and resolve application-related issues (break/fix), maintaining the reliability, accuracy, and effectiveness of quality and manufacturing data systems across all supported sites
Qualifications:
Required:
• Manufacturing fluency — understanding of manufacturing processes, quality systems, and operational workflows, able to translate floor-level needs into structured data requirements
• Power BI proficiency — advanced skill in developing interactive, multi-source dashboards and reports that communicate complex manufacturing data clearly to diverse audiences
• PowerApps and Power Automate — experienced in building workflow automation and custom applications that support quality and operations teams
• Data source architecture and pipeline development — ability to identify missing data, design new collection structures, and build integrations that bring previously uncaptured information into analytics platforms
• Systems integration knowledge — skilled in connecting and harmonizing data streams from ERP, MES, QMS, and shop floor data collection systems into unified, reliable reporting environments
• Data management — skilled in collecting, cleaning, organizing, and maintaining accurate large-scale manufacturing data sets with high attention to data integrity
• Database systems and data architecture — knowledgeable in relational database concepts, data modeling, and architecture principles that support scalable analytics solutions
• AI and emerging technology adoption — actively utilizes AI tools and new technologies to innovate data workflows, improve accuracy, and solve complex manufacturing and quality challenges
• Project leadership — experienced in managing data and systems projects independently, setting priorities, coordinating cross-functional stakeholders, and delivering results on time
• Cross-functional collaboration — highly effective working partner for process engineering, quality, operations, and IT teams — able to speak the language of the floor and the language of data with equal credibility
• Bachelor's degree in engineering, sciences, management, or arts
• 3-5 years of hands-on experience with manufacturing data systems and processes, including working with ERP, MES, and QMS platforms for data integration, analysis, and reporting.
Preferred:
• Lean / Six Sigma Green Belt or Black Belt — strongly preferred; the ability to apply structured problem-solving and variation reduction methodologies directly enhances the value of this role
• SQL experience — ability to write and modify queries for data extraction, transformation, and validation across relational databases
• Industrial engineering tools and methods — familiarity with time studies, process mapping, OEE frameworks, capacity analysis, or similar IE methodologies as they relate to data collection and analysis
• Experience with shop floor data collection systems — historian platforms, SCADA, or similar real-time manufacturing data sources
• Python or R — basic scripting for data manipulation or automation is a plus
Company:
Corning is a manufacturer of glass, ceramics, and related materials. Founded in 1851, the company is headquartered in Corning, USA, with a team of 10001+ employees. The company is currently Late Stage.

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