Full-Time

Data Engineer Lead

Deadline 9/30/26
Dallas County

Dallas County

Compensation Overview

$9.3k - $11.6k/mo

Texas, USA

In Person

Occasional travel to County sites; valid Texas driver's license required.

Category
Data & Analytics (1)
Required Skills
Data Engineering
Docker
Observability
REST APIs
Data Governance
DevOps
Requirements
  • Education and experience equivalent to a Bachelor’s degree from an accredited college or university in Computer Science, Software Engineering, or a related field.
  • Six (6) years of experience in data engineering, cloud platforms, or AI-driven data processing, with two (2) years in a technical leadership role.
  • Must possess a valid Texas Driver’s License and maintain a good driving record; will be required to provide a copy of 10-year driving history.
  • Individuals holding or considered for a position which has, or may have, access to criminal justice databases including the FBI Criminal Justice Information Systems, NCIC/TCIC and similar databases, must pass a national fingerprint-based records check prior to placement in such position and may be denied placement in such positions and/or access to such systems.
  • Occasional travel to County sites, vendor meetings, conferences, and industry events.
Responsibilities
  • Oversees the design and delivery of data pipelines, APIs, and workflows across departments and platforms.
  • Ensures high availability, scalability, and fault tolerance of enterprise data systems.
  • Aligns data services with enterprise SLAs and critical system dependencies.
  • Leads technical implementation of real-time, streaming, and event-based data architectures.
  • Supports data operations through monitoring, logging, and automated incident management.
  • Enforces data governance, access control, and privacy compliance in pipeline delivery.
  • Ensures efficient onboarding of new data sources, systems, and applications.
  • Collaborates with DevOps teams to implement CI/CD and observability pipelines.
  • Maintains operational readiness through automated recovery and version control.
  • Supports data enablement across analytics, dashboards, and AI platforms.
  • Establishes technical standards for data pipeline development, testing, and maintenance.
  • Oversees data workflow orchestration across cloud and on-prem environments.
  • Implements platform observability through metrics, logging, and alerting frameworks.
  • Drives automation of recurring workflows and code deployment processes.
  • Defines strategies for cost management, resource optimization, and scaling of cloud data services.
  • Coordinates with cybersecurity and IT operations on platform compliance and patching.
  • Leads vendor and tool evaluations to improve engineering efficiency and system reliability.
  • Guides containerization, IaC (Infrastructure as Code), and platform provisioning strategies.
  • Manages risks associated with data latency, accuracy, and source system instability.
  • Supports internal audits and compliance assessments related to data workflows.
  • Designs technical architectures for enterprise data pipelines, storage, and integrations.
  • Partners with Data Architects to define shared schemas, domain models, and frameworks.
  • Leads architectural reviews and guide the selection of tools and design patterns.
  • Architect solutions to support distributed, hybrid, and cloud-native data platforms.
  • Evaluates new technologies and approaches for ML Ops, streaming, and open data sharing.
  • Ensures interoperability between data systems, applications, and public-facing services.
  • Drives modularization and reusability of components across departments.
  • Contributes to enterprise blueprints, roadmaps, and modernization initiatives.
  • Aligns data architecture with digital, accessibility, and equity goals.
  • Guides technical solutioning in strategic cross-agency data collaborations.
  • Defines and enforce data quality standards, validation frameworks, and QA practices.
  • Builds automated systems for anomaly detection, metadata validation, and lineage tracking.
  • Guides root cause analysis and long-term resolutions for recurring data issues.
  • Promotes test-driven development, CI/CD, and code review best practices.
  • Drives adoption of monitoring tools to proactively detect and resolve pipeline issues.
  • Ensures all systems meet County standards for privacy, accessibility, and resilience.
  • Leads retrospectives and continuous improvement initiatives across the engineering lifecycle.
  • Optimizes performance of data flows, batch processes, and analytics queries.
  • Reduces manual dependencies through automation and workflow refactoring.
  • Evaluates team tools and recommend improvements to increase velocity and quality.
  • Provides day-to-day technical leadership and mentoring to data engineering staff.
  • Coordinates team assignments, project planning, and workload balancing.
  • Guides onboarding of new team members and support training across tools and platforms.
  • Fosters a team culture centered on innovation, learning, and operational excellence.
  • Promotes diversity and inclusion through equitable leadership and career development.
  • Leads communities of practice focused on data engineering or cloud platforms.
  • Identifies and addresses skill gaps and training opportunities across the team.
  • Partners with management on hiring decisions and organizational planning.
  • Sets and reviews engineering goals, OKRs, and performance metrics.
  • Encourages collaboration across engineering, analytics, and application teams.
  • Acts as the technical point of contact for enterprise data engineering initiatives.
  • Translates stakeholder needs into architecture and delivery roadmaps.
  • Partners with product owners, analysts, and executives to align on data enablement strategy.
  • Coordinates with application teams on integrations, event publishing, and system changes.
  • Supports governance and compliance teams in implementing data controls and lineage.
  • Represents the data team in executive steering committees, RFP development, or vendor assessments.
  • Ensures data delivery timelines, risks, and dependencies are clearly communicated.
  • Participates in public-sector partnerships and cross-agency data collaborations.
  • Supports data literacy efforts by enabling clean, documented datasets for business users.
  • Advocates for the role of data engineering in supporting County priorities and digital equity.
  • Contributes to proposals, RFPs, or funding requests involving enterprise data initiatives.
  • Performs other duties as assigned.
Desired Qualifications
  • Master’s degree in Computer Science, Software Engineering, or a related field is preferred.
  • Certifications in Cloud data platforms such as Azure Data Engineer, AWS Big Data, or Google Cloud Platform Professional Data Engineer are preferred.
  • Certifications in Airflow, Snowflake, Databricks, or similar platform are preferred.
  • Knowledge of AI, machine learning, and data automation strategies is desirable.
  • Knowledge of cloud-native data architecture, DevOps, and enterprise analytics enablement is desirable.
  • Ability to translate strategic goals into scalable engineering delivery and to lead cross-functional technical teams is desirable.
  • Ability to communicate and collaborate with executive leadership is desirable.

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