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Ability to Obtain Public TrustSenior Data Engineer (Analytics & Applied AI)
About the role
We build production data platforms that turn large, complex datasets into reliable analytics and applications. We are looking for a Senior Data Engineer who is equally strong as a software engineer and a data engineer — someone who writes production-quality Python, designs and orchestrates cloud data pipelines, and stands behind the correctness of the numbers those pipelines produce.
This is a senior, high-autonomy role. You will own systems end to end, make architecture decisions, and drive work forward independently with minimal oversight. You should be comfortable setting technical direction, not just executing tickets. Experience with agentic coding assistants (using AI tooling to move faster while keeping quality high) is expected — we build with modern tooling and want engineers who leverage it well.
Data analytics fluency is required — you reason about the numbers, not just move them.
What you'll do
- Own data platforms end to end — from source ingestion through transformation to the analytics and application layers that consume them — with correctness, idempotency, and auditability as first-class requirements.
- Write production Python and build software, not just SQL — services, APIs, tooling, and well-tested, maintainable code. This is a software-engineering role as much as a data role.
- Design and orchestrate cloud data pipelines on AWS with scheduling, retries, data-quality gates, and clean failure handling.
- Model data for analytics and application serving — dimensional/fact modeling, grain decisions, slowly-changing dimensions, idempotent recompute patterns, and the distinction between an analytical warehouse and an application system-of-record.
- Implement data-quality and reconciliation checks that run every pipeline and gate publication — you don't ship numbers you can't defend.
- Drive architecture and technical decisions independently — evaluate tradeoffs, choose approaches, and lead implementation with limited supervision.
- Use agentic coding assistants effectively — leverage AI development tooling to accelerate delivery while maintaining code quality and review discipline.
Grow into applied AI/ML — entity resolution / record linkage, LLM-assisted analytics over structured and unstructured data, and anomaly/risk scoring as the work expands. *
What You Will Need
- SIX (6+) years in data engineering and/or software engineering building and operating production systems.
- University Degree and minimum SIX (6) years of prior relevant experience; FOUR (4) years of relevant experience may be substituted for formal education.
- Strong software engineering — production-quality Python, clean architecture, testing, code review, and CI/CD. You build software, not just scripts.
- Expert SQL — window functions, complex joins, aggregation-grain reasoning, and query performance.
- AWS data & compute stack — hands-on with several of: Glue, Step Functions, S3, Redshift, Aurora/RDS, Lambda, EventBridge, ECS/Fargate, IAM.
- Data modeling — dimensional/analytical modeling, grain, SCD, and idempotent recompute patterns.
- Data-quality discipline — you validate outputs, reconcile totals, and defend your numbers.
- Infrastructure as code — Terraform or CDK in a real project.
- High autonomy — proven ability to own systems and drive them to completion independently, with sound judgment on architecture and tradeoffs.
- Agentic coding assistants — demonstrated, effective use of AI coding tools in a real workflow.
- Analytics fluency — profile a dataset, spot anomalies, reason about what a metric means, and explain discrepancies.
Must be able to obtain and maintain a Federal or DoD "public trust"; candidates must obtain approved adjudication of their public trust prior to onboarding with Guidehouse. Candidates with an active public trust or suitability are preferred *
What Would Be Nice to Have
- Applied AI / ML — practical LLM use (RAG, structured extraction from unstructured text), embeddings, or ML risk-scoring; strong interest counts even without deep production experience.
- Agent frameworks — AWS Strands Agents, Bedrock AgentCore, or similar for building tool-using / multi-agent systems.
- Entity resolution / record linkage — probabilistic matching
- API development — FastAPI or similar; enough front-end awareness to collaborate with UI engineers.
- DuckDB / Parquet-based data flows and data-lake table formats (Iceberg/Delta).
- Experience in regulated or government data environments.
The annual salary range for this position is $113,000.00-$188,000.00. Compensation decisions depend on a wide range of factors, including but not limited to skill sets, experience and training, security clearances, licensure and certifications, and other business and organizational needs. What We Offer:
Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace.
Benefits include:
- Medical, Rx, Dental & Vision Insurance
- Personal and Family Sick Time & Company Paid Holidays
- Parental Leave
- 401(k) Retirement Plan
- Group Term Life and Travel Assistance
- Voluntary Life and AD&D Insurance
- Health Savings Account, Health Care & Dependent Care Flexible Spending Accounts
- Transit and Parking Commuter Benefits
- Short-Term & Long-Term Disability
- Tuition Reimbursement, Personal Development, Certifications & Learning Opportunities
- Employee Referral Program
- Corporate Sponsored Events & Community Outreach
- Care.com annual membership
- Employee Assistance Program
- Supplemental Benefits via Corestream (Critical Care, Hospital Indemnity, Accident Insurance, Legal Assistance and ID theft protection, etc.)
- Position may be eligible for a discretionary variable incentive bonus
About Guidehouse
Guidehouse is an Equal Opportunity Employer–Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation.
Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco.
If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1-571-633-1711 or via email at [email protected]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation.
All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains including @guidehouse.com or [email protected]. Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process.
If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse’s Ethics Hotline. If you want to check the validity of correspondence you have received, please contact [email protected]. Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant’s dealings with unauthorized third parties.
Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.
Salary Context
This $113K-$188K range is above the median for Data Engineer roles in our dataset (median: $145K across 47 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 11,520 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Guidehouse, 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 $177,000 based on 195 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($150K) sits 15% below the category median. Disclosed range: $113K to $188K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Guidehouse AI Hiring
Guidehouse has 8 open AI roles right now. They're hiring across Data Engineer, Data Scientist, AI/ML Engineer. Positions span Washington, DC, US, Philadelphia, PA, US, Beavercreek, OH. Compensation range: $133K - $188K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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