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Data Engineering Lead Jobs (NOW HIRING)

AlloBaas

Data Engineering Lead

Lakewood, OH · On-site

The Data Engineering Lead plays a critical and strategic role in advancing FMHC's enterprise data transformation and analytics enablement efforts. This role provides technical leadership, direction ...

ECS Resource Group

Data Engineering Lead

Newark, NJ · Hybrid

£60K - £90K/yr

Data Engineering Lead Newark - Flexible Hybrid Working Permanent Package: up to 90'000k We are recruiting for an experienced Data Engineering Lead, working for one of the UK's leading technology and ...

ALLOBAAS

Data Engineering Lead

Lakewood, OH · On-site

The Data Engineering Lead plays a critical and strategic role in advancing FMHC's enterprise data transformation and analytics enablement efforts. This role provides technical leadership, direction ...

Ignite IT

Data Engineering Lead

Suitland, MD

The Data Engineering Lead will ensure data integrity, performance, and visibility across a system-of-systems modernization initiative, while providing technical leadership for data modeling ...

Smart Synergies

Data Engineering Lead

Glendale, AZ · On-site

We are seeking an experienced and highly skilled Data Engineering Lead to spearhead our data initiatives, with a primary focus on Azure Data Lake and its associated ecosystem. The ideal candidate ...

Ignite IT

Data Engineering Lead

Suitland, MD · On-site

The Data Engineering Lead will ensure data integrity, performance, and visibility across a system-of-systems modernization initiative, while providing technical leadership for data modeling ...

QXO

Data Engineering Lead

East Rutherford, NJ

$168K - $210K/yr

We are looking for a Data Engineering Lead to design, build, and operate scalable, cloudnative data platforms and data products on Google Cloud. This role combines handson engineering with technical ...

FMHC

AlloBaas - Data Engineering Lead

Lakewood, OH

The Data Engineering Lead plays a critical and strategic role in advancing FMHC's enterprise data transformation and analytics enablement efforts. This role provides technical leadership, direction ...

QXO

Data Engineering Lead

East Rutherford, NJ · On-site

$168K - $210K/yr

We are looking for a Data Engineering Lead to design, build, and operate scalable, cloud-native data platforms and data products on Google Cloud. This role combines hands-on engineering with ...

Dorman Products, Inc.

Data Engineering Lead

Colmar, PA · On-site

$113K - $136K/yr

Job Summary Dorman Products is seeking a Data Engineering Lead to build and scale modern data engineering capabilities while supporting our existing enterprise data environment. This role is hands-on ...

Dorman Products

Data Engineering Lead

Colmar, PA · Hybrid

$113K - $136K/yr

Job Summary Dorman Products is seeking a Data Engineering Lead to build and scale modern data engineering capabilities while supporting our existing enterprise data environment. This role is ...

Dorman Products

Data Engineering Lead

Colmar, PA · Hybrid

$113K - $136K/yr

Job Summary Dorman Products is seeking a Data Engineering Lead to build and scale modern data engineering capabilities while supporting our existing enterprise data environment. This role is handson ...

LVTLABS\-Magus

Data Engineering Lead

Alpharetta, GA · On-site

\n \n \n \n \n We are looking for a highly skilled Data Engineering Lead to spearhead the development and optimization of advanced data solutions. This role requires an individual with expertise in ...

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Data Engineering Lead information

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$30

$70

$95

How much do data engineering lead jobs pay per hour?

As of Jul 16, 2026, the average hourly pay for data engineering lead in the United States is $70.08, according to ZipRecruiter salary data. Most workers in this role earn between $60.58 and $78.61 per hour, depending on experience, location, and employer.

What does a data engineering lead do?

A data engineering lead oversees the design, development, and maintenance of data pipelines and infrastructure to ensure reliable data flow for analytics and business needs. They coordinate with data scientists and analysts, utilize tools like SQL, Spark, and cloud platforms, and often require strong programming skills and leadership experience to manage data teams effectively.

What are Data Engineering Leads?

Data Engineering Leads are senior professionals responsible for overseeing data engineering teams and projects. They design, build, and maintain data infrastructure, ensuring data is accessible, reliable, and secure for analytics and business use. Typically, they coordinate with data scientists, analysts, and other stakeholders to define data requirements and implement best practices in data management. Their role also involves mentoring team members, choosing appropriate technologies, and ensuring the scalability and performance of data systems.

What is the difference between Data Engineering Lead vs Data Engineer?

AspectData Engineering LeadData Engineer
Required CredentialsBachelor's or Master's in CS, certifications like AWS, GCP, or AzureBachelor's in CS, related certifications optional
Work EnvironmentLeads teams, manages projects, collaborates with stakeholdersDevelops data pipelines, implements data solutions, collaborates with teams
Employer & Industry UsageUsed in organizations with data teams, analytics, and BI departmentsEntry to mid-level roles in data-focused companies

The Data Engineering Lead typically oversees data projects, manages teams, and coordinates with stakeholders, requiring leadership skills and experience. Data Engineers focus on building and maintaining data pipelines and infrastructure. While both roles require similar technical skills, the Lead role involves more strategic and managerial responsibilities.

What engineers make $500,000?

Senior data engineers with extensive experience, advanced skills in cloud platforms, and expertise in big data tools can earn $500,000 or more annually, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires strong technical knowledge, leadership abilities, and a track record of delivering complex data solutions.

What engineers make $300,000 a year?

Senior data engineers, especially those with extensive experience, advanced skills in cloud platforms, and expertise in big data tools like Spark and Hadoop, can earn $300,000 or more annually. Compensation often depends on location, industry, company size, and additional certifications or specialized knowledge.

What are the key skills and qualifications needed to thrive as a Data Engineering Lead, and why are they important?

To thrive as a Data Engineering Lead, you need strong expertise in data modeling, ETL pipeline development, and database architecture, often supported by a degree in computer science or a related field. Familiarity with big data technologies like Hadoop, Spark, and cloud platforms such as AWS or Azure, as well as certifications in these systems, is highly valuable. Excellent leadership, problem-solving, and communication skills help in managing teams and collaborating with stakeholders. These competencies ensure efficient data infrastructure development, drive data-driven decision-making, and foster innovation within organizations.

What are some common challenges faced by a Data Engineering Lead when managing large-scale data infrastructure projects?

A Data Engineering Lead often encounters challenges such as balancing short-term business needs with long-term architectural goals, ensuring data quality across multiple sources, and managing the complexity of integrating new technologies with existing systems. They also need to coordinate effectively with cross-functional teams, including Data Scientists, Analysts, and DevOps, to align on project priorities and timelines. Additionally, leading and mentoring a team of engineers requires strong communication and organizational skills to foster collaboration and continuous improvement.

Can a data engineer make 200k?

Senior data engineers with extensive experience, advanced skills in tools like Spark and cloud platforms, and certifications can reach or exceed a $200,000 salary, especially in high-cost-of-living areas or large organizations. Entry-level or mid-level data engineers typically earn less, with salaries increasing with expertise, leadership responsibilities, and specialization in areas like data architecture or machine learning.
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What cities are hiring for Data Engineering Lead jobs? Cities with the most Data Engineering Lead job openings:
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Data Engineering Lead jobs near you
Infographic showing various Data Engineering Lead job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $145,772 per year, or $70.1 per hour.

Data Engineering Lead

AlloBaas

Lakewood, OH • On-site

Full-time

Re-posted 18 days ago

Job description

Position Summary:
The Data Engineering Lead plays a critical and strategic role in advancing FMHC's enterprise data transformation and analytics enablement efforts. This role provides technical leadership, direction, and oversight for data engineering activities across data platforms, data pipelines, integrations, data warehouse operations, middleware/Azure Enterprise Service Bus capabilities, reporting modernization, and enterprise data governance practices.
In partnership with business stakeholders and third-party vendors, the Data Engineering Lead is responsible for guiding the design, development, support, and continuous improvement of scalable, secure, reliable, and well-documented data solutions. This role provides day-to-day technical direction to Data Engineer I and Data Engineer II roles, supports their professional development, and serves as a primary escalation point for complex technical, operational, and data quality issues.
Duties and Responsibilities:
  • Leadership & Strategic Oversight.
  • Lead and mentor a team of Data Engineering and Analyst staff, providing coaching, performance feedback, and professional development opportunities.
  • Serve as the primary escalation point for complex data engineering, data platform, integration, data quality, and production support issues.
  • Establish and communicate data engineering standards, best practices, development patterns, documentation expectations, and operational procedures.
  • Lead regular technical planning, solution review, and prioritization discussions to ensure timely execution of data engineering initiatives.
  • Promote a collaborative, high-performing team culture focused on accountability, quality, continuous improvement, operational excellence, and knowledge sharing.
  • Support professional development of data engineering team members through coaching, feedback, technical guidance, and skill development opportunities.
  • Serve as the primary escalation point for team-level challenges, providing guidance,resolution, and technical mentorship as needed.
  • Collaborate with leadership to define key product objectives, KPIs, and performance outcomes.

-Data Platform Ownership
  • Lead and assist in administration, optimization, and operational oversight of Snowflake and related cloud data platforms.
  • Oversee table structures, role and permission management, performance tuning, cost optimization, capacity planning, and platform configuration standards.
  • Provide leadership for Data Warehouse, Middleware/Azure Enterprise Service Bus, Cognos, Limagito, and related analytics platform operations.
  • Ensure data platforms are scalable, secure, reliable, cost-effective, and aligned with business and technology goals. Recommend, implement, and maintain best practices for data platform engineering, platform operations, and environment management.
  • Oversee bug tracking, error resolution, and continuous optimization
  • Design, implement, and oversee intake and delivery processes related to Business Intelligence, Analytics, Reporting, and Data Provider sourcing initiatives.
  • Translate product roadmap features into well-defined requirements, user stories, and acceptance criteria.
  • Support creation of test scripts, QA planning, and post-launch performance tracking Data Pipeline, Integration & Architecture Leadership
  • Lead and assist in implementing the design, development, implementation, and support of scalable data pipelines, data conversions, ingestion processes, integrations, and data vendor feeds.
  • Oversee data extraction, ingestion, transformation, normalization, anonymization, validation, loading, and reconciliation processes.
  • Guide integration activities involving APIs, middleware, Azure ESB, core banking systems, digital banking platforms, and third-party data sources.
  • Maintain and communicate enterprise data architecture documentation, including data flows, system dependencies, integration diagrams, platform architecture, and process maps.
  • Data Quality, Governance, Security & Compliance
  • Lead the implementation and continuous improvement of data validation, reconciliation, monitoring, and quality control processes.
  • Establish standards for data quality checks, issue tracking, data lineage, data mappings, business rules, and data documentation.
  • Enforce data governance policies to ensure data is secure, consistent, accurate, reliable, and compliant with applicable financial regulations and cybersecurity standards.
  • Ensure appropriate controls are considered in data access, data sharing, reporting, integration, and platform administration processes.
  • Operational Support & Continuous Improvement & Non-Technical Ownership
  • Assist with internal stakeholder relationships and partner to ensure alignment and best practice implementation (e.g., marketing, contact center, compliance, fraud, risk).
  • Oversee and track project and program roadmaps, identifying risks, dependencies, and cross-functional impact. Lead backlog prioritization in alignment with business goals and customer feedback.
  • Manage and coordinate with third-party vendors, implementation partners, and technology providers to support data integrations, platform enhancements, issue resolution, and ongoing operations. Ensure high performance of vendors by monitoring work, evaluating contracts and assessing vendor options.
  • Ensure timely delivery of all risk assessments, required reporting, performance metrics, and strategic insights.
  • Lead and assist in operational support practices for data platforms, pipelines, integrations, reporting systems, and related data services including ITSM and documentation best practices.
  • Support continuous improvement and automation of governance practices, operational controls, and data management maturity across the organization.
  • Ensure upstream & downstream data requirements are identified and addressed. Ensure timely resolution of escalated platform, pipeline, data quality, reporting, and integration issues.
  • Develop and improve monitoring, alerting, support documentation, production readiness, release support, and change management practices. Support testing, QA planning, validation, deployment readiness, and post-implementation review for data-related changes.
  • Stay informed of industry trends and emerging technologies to drive innovation and competitive advantage
  • Monitor and manage platform consumption expenses and budgets.
  • Complies with all applicable banking laws and regulations, including, but not limited to the Bank Secrecy Act, USA Patriot Act, and related anti-money laundering statutes, and federal consumer protection legislation and regulations. Builds working knowledge of all applicable laws and regulations.
  • Other duties as required.

The duties outlined above are a summary and may not be an exhaustive or comprehensive list of all possible responsibilities, tasks, and duties. All job descriptions may be amended at any time at the sole discretion of FMHC.
Requirements
Qualifications and Skills:
  1. 5+ years of progressive experience in data engineering, data platform engineering, data warehouse operations, ETL/ELT development, data integration, or related data technology roles.
  2. 3+ years of experience providing technical leadership, functional work direction, mentorship, solution review, or escalation support for data engineering or technology teams.
  3. 5+ years of hands-on experience designing, developing, supporting, and optimizing data pipelines, ingestion processes, data conversions, data quality processes, and data loading patterns.
  4. 5+ years of experience with SQL, relational databases, data modeling, data mapping, data validation, reconciliation, and data analysis.
  5. 3+ years of hands-on experience administering, supporting, or optimizing Snowflake or comparable cloud data platforms.
  6. Experience with cloud and/or on-premises database environments, including platform configuration, access management, performance considerations, and operational support.
  7. Experience with data integration technologies, APIs, middleware, Azure Enterprise Service Bus, or similar integration platforms.
  8. Experience with development lifecycle practices and tools such as C#, GitHub or similar source control, Azure DevOps, CI/CD pipelines, and deployment management.
  9. Experience with reporting, analytics, and business intelligence environments such as Cognos, Tableau, Power BI, or similar tools.
  10. Experience leading or supporting data migration, reporting modernization, platform implementation, and data validation initiatives.
  11. Demonstrated ability to document and communicate enterprise data architecture, system dependencies, data flows, business rules, technical requirements, process maps, and support procedures.
  12. Demonstrated ability to translate business needs into scalable, secure, reliable, and well-documented technical data solutions.
  13. Strong understanding of data governance, data security, data quality, regulatory compliance, and cybersecurity considerations in a financial services environment.
  14. Bachelor's degree in Information Technology, Computer Science, Data Analytics, Business Information Systems, Engineering, or a related field, or equivalent combination of education and experience.

Preferred Experience Includes:
  1. Prior work within banking, fintech, mortgage, insurance, or other regulated financial services environments.
  2. Experience supporting data engineering capabilities for digital banking, core banking, customer-facing financial platforms, or enterprise analytics.
  3. Experience with Snowflake cost optimization, performance tuning, role-based access controls, data sharing, and cloud data platform governance.
  4. Experience with Azure data services, Azure DevOps, Azure Enterprise Service Bus, APIs, middleware, and enterprise integration patterns.
  5. Experience with Lean/Six Sigma, process analysis and design, systems architecture, or operational process improvement.
  6. Experience with Agile and Waterfall delivery methodologies.
  7. Experience with collaboration and work management tools such as Smartsheet, Jira, Confluence, Miro, or similar.
  8. Experience developing standards, reusable patterns, technical documentation, production support procedures, and team operating practices.
  9. Experience coordinating vendor relationships, technical escalations, implementation partners, or third-party data providers.
  10. Experience supporting audit, regulatory, risk, compliance, cybersecurity, or data governance activities within a financial institution.

Necessary Competencies:
  1. Critical thinking
  2. Leads Courageously
  3. Initiative
  4. Creativity
  5. Communication
  6. Organizational Skills
  7. Interpersonal Awareness
  8. Decisiveness

Physical Environment:
  • This position is performed in a corporate office (Lakewood, OH), hybrid, or remote setting:
  1. If fully remote: must be willing to travel
  • This position will requires the ability to work flexible days/times including occasionally working beyond normal business hours on an "as needed" basis.
  • While performing the duties of this job, the employee is regularly required to lift, walk, stand, sit, bend, reach with hands and arms, climb, push/pull, use hands, and see, hear and speak.
  • The employee must occasionally lift and/or move up to 25 pounds.
  • The noise level in the work environment is usually quiet to moderate.

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