Interested in this Data Engineer role at Slalom Consulting?
Apply Now →Skills & Technologies
About This Role
General Information
Locations
- Atlanta, GA
- Boston, MA
- Charlotte, NC
- Chicago, IL
- Columbus, OH
- Dallas, TX
- Detroit, MI
- Houston, TX
- Miami, FL
- Minneapolis, MN
- Nashville, TN
- New Brunswick, NJ
- New York, NY
- St. Louis, MO
Business Function Data & analytics Date posted Friday, September 25, 2026 Ref # JBR0001942 Description and Requirements
Job Description Managing Director, Data Engineering & AI Who You'll Work With As a Managing Director in Slalom's Data & AI practice, you will lead the growth of our Data Engineering & AI business by helping clients modernize their data foundations and accelerate AI adoption. You will partner with executive stakeholders, technology leaders, alliance partners, and Slalom teams to shape strategy, solve complex business challenges, and deliver measurable outcomes.
This role blends business development, executive consulting, technical leadership, and people leadership. You will build trusted client relationships, grow strategic accounts, develop high-performing teams, and help organizations transform through modern data platforms, cloud technologies, analytics, and AI.
What You'll Do
Grow the Business
- Build and expand executive relationships with CIOs, CTOs, CDOs, CAIOs, and business leaders.
- Drive revenue growth through strategic account development, pipeline creation, and complex pursuits.
- Lead executive discovery sessions, solution workshops, and business case development.
- Partner across Slalom capabilities to shape integrated transformation solutions that deliver measurable business value.
- Develop and evolve go-to-market offerings that strengthen Slalom's position in Data Engineering & AI.
Lead Data & AI Transformation
- Advise clients on enterprise data strategy, data modernization, analytics, and AI adoption.
- Help organizations build the data foundations required to enable machine learning, generative AI, and intelligent automation.
- Guide clients through AI readiness, governance, operating models, and enterprise-scale implementation.
- Connect business priorities to technology investments, architecture decisions, and transformation roadmaps.
- Serve as a trusted advisor on responsible AI, risk management, and value realization.
Provide Technical Leadership
- Design and communicate enterprise data and AI architectures with both executive and technical audiences.
- Facilitate architecture workshops and whiteboard sessions with client stakeholders and engineering teams.
- Lead solution strategy for modern data platforms spanning ingestion, transformation, governance, analytics, machine learning, and AI.
- Advise clients on cloud-native architectures across AWS, Azure, and Google Cloud.
- Ensure solutions are scalable, secure, governed, and aligned to business objectives.
Build Modern Data & AI Solutions
- Shape solutions leveraging technologies such as Databricks, Snowflake, dbt, Dataiku, Fivetran, Matillion, and related platforms.
- Guide enterprise adoption of lakehouse architectures, data products, data governance, metadata management, and modern engineering practices.
- Lead conversations around generative AI, large language models (LLMs), agentic AI, retrieval-augmented generation (RAG), and AI-enabled business processes.
- Partner with delivery teams to ensure architectural integrity from strategy through implementation.
Develop Talent & Practice Capability
- Build, mentor, and inspire high-performing teams of leaders, architects, engineers, and consultants.
- Coach teams on consulting excellence, technical leadership, solution selling, and executive communication.
- Establish standards, frameworks, and reusable assets that improve consistency and delivery quality.
- Foster an inclusive, collaborative culture focused on growth, innovation, and continuous learning.
Elevate Slalom in the Market
- Represent Slalom as a thought leader in data engineering, cloud platforms, analytics, and AI.
- Speak at conferences, executive forums, industry events, and partner programs.
- Contribute to market-facing thought leadership, client offerings, and strategic partnerships.
- Strengthen relationships across key ecosystem partners including Databricks, Snowflake, Microsoft, AWS, Google Cloud, and leading AI platform providers.
What You'll Bring
Leadership & Consulting Experience
- 15+ years of experience leading data, analytics, AI, or cloud transformation initiatives.
- Proven success growing consulting businesses, practices, portfolios, or strategic client accounts.
- Experience influencing executive stakeholders and leading complex business and technology transformations.
- Demonstrated ability to lead large pursuits, develop pipeline, and drive revenue growth.
- Track record of building, developing, and mentoring high-performing teams.
- Exceptional communication, storytelling, and relationship-building skills.
Data Engineering & AI Expertise
- Deep expertise in data engineering, data architecture, data platforms, and cloud data ecosystems.
- Experience designing and implementing enterprise-scale data platforms and modern data architectures.
- Strong understanding of lakehouse architectures, data governance, data products, metadata management, data quality, and modern engineering practices.
- Experience with Databricks, Snowflake, and cloud-native technologies on AWS, Azure, and/or Google Cloud.
- Familiarity with machine learning, generative AI, LLMs, AI agents, RAG architectures, and enterprise AI adoption patterns.
- Ability to translate complex technical concepts into clear business outcomes and executive recommendations.
About Us Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value, in all ways, always. From strategy through delivery, our agile teams across 52 offices in 12 countries partner with clients to co-create powerful customer experiences, modern ways of working, and meaningful impact. What sets us apart? We believe work should be challenging and fulfilling, not perfect, but possible. That’s why we prioritize purpose, flexibility, connection, and recognition, so our people can thrive and love what they do, most days. Compensation and Benefits Slalom prides itself on helping team members thrive in their work and life. As a result, Slalom is proud to invest in benefits that include meaningful time off and paid holidays, parental leave, 401(k) with a match, a range of choices for highly subsidized health, dental, & vision coverage, adoption and fertility assistance, and short/long-term disability. We also offer yearly $350 reimbursement account for any well-being-related expenses, as well as discounted home, auto, and pet insurance. In addition, individuals may be eligible for an annual discretionary bonus. Actual compensation will depend upon an individual’s skills, experience, qualifications, location, and other relevant factors. The salary pay range is subject to change and may be modified at any time. We are committed to pay transparency and compliance with applicable laws. If you have questions or concerns about the pay range or other compensation information in this posting, please contact us at: [email protected]. Please note, this recipient is not able to support recruitment inquiries beyond this purpose. EEO and Accommodations Slalomisanequalopportunityemployerandiscommittedtoattracting,developingandretaininghighlyqualifiedtalentwhoempowerourinnovativeteamsthroughuniqueperspectivesandexperiences.Allqualifiedapplicantswillreceiveconsiderationforemploymentwithoutregardtorace,color,religion,sex,nationalorigin,disabilitystatus,protectedveterans’status,oranyothercharacteristicprotectedbyfederal,state,orlocallaws.Slalomwillalsoconsiderqualifiedapplicationswithcriminalhistories,consistentwithlegalrequirements.Slalomwelcomesandencouragesapplicationsfromindividualswithdisabilities.Reasonableaccommodationsareavailableforcandidatesduringallaspectsoftheselectionprocess.Pleaseadvisethetalentacquisitionteamorcontact[email protected]ifyourequireaccommodationsduringtheinterview**process.
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 9,653 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Slalom Consulting, 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 $177,825 based on 160 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Slalom Consulting AI Hiring
Slalom Consulting has 12 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Architect. Positions span Houston, TX, US, Atlanta, GA, US, Denver, CO, US. Compensation range: $150K - $285K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.