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About This Role
Benefits:
- HYBRID
- Competitive salary
- Opportunity for advancement
Technical Project Manager \| Data Engineering \& Data Science
Location: Dallas, TX Hybrid (Onsite \+ Remote)
Employment Type: Full Time / Contract (W2\)
Duration: 12\+ Months (Ongoing)
Cloud Environment: Amazon Web Services (AWS)
Team Structure: US Onsite Teams and Offshore GCC Teams
Compensation: Competitive Market Rate
We are seeking an experienced Technical Project Manager with strong expertise in Data Engineering and Data Science programs to lead enterprise initiatives within a large commercial airline environment.
In this role, you will coordinate project delivery between onsite leadership in the United States and offshore Global Capability Center (GCC) teams, driving AWS\-based Data Engineering, Analytics, and Machine Learning initiatives from planning through successful delivery.
Required Qualifications
Technical Skills
- Experience managing Data Engineering and/or Data Science projects
- Strong understanding of AWS data services: Amazon S3 • AWS Glue • Amazon Redshift • Amazon EMR • Amazon Athena • Amazon SageMaker • AWS Lambda • AWS Step Functions
- Knowledge of ETL/ELT frameworks and modern data pipeline architecture
- Familiarity with Machine Learning lifecycle management
- Experience with Agile delivery methodologies (Scrum or SAFe)
- Experience using Jira, Confluence, or similar project management tools
- Understanding of cloud security, data governance, and data quality best practices
Project Management
- Experience managing complex enterprise technology programs
- Experience coordinating onsite and offshore GCC teams
- Strong skills in project planning, dependency management, risk management, and stakeholder communication
- Ability to communicate technical concepts effectively to executive leadership
Professional Skills
- Excellent verbal and written communication
- Strong organizational and planning abilities
- Ability to manage multiple priorities in a fast\-paced environment
- Comfortable working with cross\-functional and multicultural teams
Key Responsibilities
Project Delivery
- Lead end\-to\-end delivery of Data Engineering and Data Science initiatives
- Define project scope, timelines, milestones, and success metrics
- Manage project plans, RAID logs, risk registers, and executive status reporting
- Lead Agile ceremonies including sprint planning, stand\-ups, reviews, and retrospectives
Offshore Team Coordination
- Serve as the primary liaison between onsite stakeholders and offshore GCC teams
- Coordinate work across multiple time zones
- Manage resource planning, priorities, escalations, and delivery governance
- Ensure transparency and accountability across distributed teams
Technical Leadership
AWS Technologies: Amazon S3 • AWS Glue • Amazon Redshift • Amazon EMR • AWS Lambda • Amazon SageMaker • Amazon Athena • AWS Step Functions
Additional responsibilities include:
- Partner with Data Engineers and Data Scientists
- Support architecture reviews and data pipeline planning
- Coordinate Machine Learning model deployments
- Ensure compliance with cloud security, data governance, and data quality standards
Stakeholder Management
- Present project updates to executive leadership
- Act as the primary point of contact for business and technology stakeholders
- Identify project risks and drive mitigation plans
- Facilitate collaboration across Engineering, Analytics, Operations, and IT teams
Preferred Qualifications
Certifications : PMP • PMI\-ACP • Certified Scrum Master (CSM) • SAFe • AWS Cloud Practitioner • AWS Solutions Architect
Industry Experience :Commercial Airlines • Travel • Transportation • Logistics
Additional Technical Experience : Kafka • Amazon Kinesis • Tableau • Amazon QuickSight • Microsoft Power BI • DataOps • MLOps
Domain Knowledge :Passenger Operations • Revenue Management • Fleet Analytics • Loyalty Programs
Before You Apply
Please ensure you meet most of the following qualifications:
- Experience managing Data Engineering or Data Science programs
- Strong understanding of AWS data services
- Experience leading Agile project teams
- Experience coordinating onsite and offshore teams
- Strong stakeholder management and executive communication skills
- Ability to work in a Hybrid environment
- Valid authorization to work in the United States under one of the accepted work authorization categories
Flexible work from home options available.
Salary Context
This $124K-$145K range is below the median for Data Engineer roles in our dataset (median: $153K across 35 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Select Minds LLC, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($135K) sits 27% below the category median. Disclosed range: $124K to $145K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Select Minds LLC AI Hiring
Select Minds LLC has 1 open AI role right now. They're hiring across Data Engineer. Based in Dallas, TX, US. Compensation range: $145K - $145K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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