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About This Role
Job Summary:
The IT Data Engineering Manager, AI Initiatives is expected to use strong business acumen, technical depth, and leadership skills to guide the Data Engineering team in designing, building, and operating the data pipelines and infrastructure that power the Firm's artificial intelligence (AI) and machine learning (ML) initiatives. This role partners with data scientists, AI/ML engineers, and business stakeholders to translate AI use cases into scalable data solutions, including data ingestion, feature engineering, model training and evaluation datasets, and retrieval-augmented generation (RAG) pipelines that support large language models (LLMs).
This role manages the Data Engineering team to identify, curate, and prepare the Firm's data assets so they are trustworthy, well-governed, and ready for use in AI applications, driving better operational efficiencies and better client outcomes through data-driven, AI-enabled insights and actions. In collaboration with data architects, data scientists, and other IT teams (such as Business Analysts, MLOps Engineers, and Project Managers), this role helps develop standard processes, procedures, and guidelines that ensure the effective and efficient delivery of sustainable AI data solutions, following design standards, responsible AI practices, and appropriate IT and user testing.
Finally, this role provides support in developing strategies, policies, and governance models to ensure data used in AI systems remains accurate, timely, secure, and relevant to the Firm's AI strategy and goals.
Job Duties:
- Ensures the Data Engineering team works effectively with data scientists, AI/ML engineers, and data architects to appropriately define and design data pipelines that support the Firm's AI initiatives
- Continually increases the maturity of data engineering practices and processes to ensure proper planning, controls, documentation, and monitoring are in place to deliver high-quality, sustainable data products for AI and analytics use cases
- Collaborates with project managers and AI/ML teams to provide effort estimates, and provides technical leadership in the design of data pipelines, feature stores, and data solutions supporting model training, fine-tuning, and inference
- Acts as a subject matter expert and catalyst to educate the organization on the Firm's data platforms and best practices as they relate to the usage, hygiene, and readiness of data for AI and generative AI applications
- Meets regularly with line of business customers and data science teams to gain a better understanding of their evolving AI use cases and data needs in order to identify potential AI-related data projects
- Works with data scientists and AI engineers to build and maintain pipelines that ingest, cleanse, transform, and structure data for machine learning models, large language models (LLMs), and generative AI solutions, including retrieval-augmented generation (RAG) and vector database integration
- Builds and maintains constructive working relationships with project team members, vendors, and other departments involved with AI projects, systems, and IT strategies
- Ensures data governance, data quality, and responsible AI principles (including data privacy, bias mitigation, and appropriate use of client and Firm data) are followed throughout the course of day-to-day data usage and AI solution design
- Identifies and executes continuous improvement efforts regarding data and AI operations, including monitoring for data drift and pipeline reliability, to ensure the Firm's AI systems remain accurate and timely
- Works closely with the Information Security, Compliance, and Data Governance teams to ensure the Firm's AI data strategy and technologies meet or exceed both internal policies and external regulatory requirements
- Collaborates with BDO Global and other BDO member firms to ensure standards between organizations are followed in support of reusable AI/ML models, feature stores, and rapid deployment of shared AI technology
- Evaluates and recommends emerging data and AI platforms, tools, and services (such as cloud-based AI/ML services, vector databases, and orchestration frameworks) to support scalable, secure AI initiatives
- Understands and follows project management basics and project management office policies and procedures
- Other duties as required
Supervisory Responsibilities:
- Supervises Data Engineering team members supporting AI initiatives
Qualifications, Knowledge, Skills and Abilities:
Education:
- High School Diploma or GED, required
- Bachelor's degree in Computer Science, Data Science, Information Management, Mathematics, or Statistics, preferred
Experience:
- Six (6) or more years working as a data engineer, analyst, or extract/transform/load (ETL) developer, required
- Three (3) or more years of demonstrable proficiency in successfully leading other developers or engineers in delivering complex data integration and/or AI/ML data solutions, required
- Two (2) or more years working on data pipelines, feature engineering, or data preparation directly supporting machine learning, generative AI, or large language model (LLM) initiatives, required
- Experience working with vector databases, embeddings, or retrieval-augmented generation (RAG) architectures, preferred
- Experience with Python and common data/ML engineering frameworks (e.g., Apache Spark, Airflow, Databricks), preferred
- Experience working within a large, complex enterprise with significant regulatory and compliance requirements, preferred
- Professional service firm experience, preferred
License/Certifications:
- N/A
Software:
- Strong proficiency in the use of the Microsoft Office suite, specifically Excel, required
- Three (3) or more years working with PowerBI, Tableau, or similar data visualization products, required
- Demonstrable proficiency in developing and understanding custom and ad-hoc SQL queries, required
- Three (3) or more years working within commercial cloud platforms such as Amazon Web Services or Microsoft Azure, including their AI/ML services (e.g., Azure OpenAI Service, Azure AI Foundry, AWS Bedrock, AWS SageMaker), preferred
- Familiarity with AI/ML platforms and tooling (e.g., Databricks, Fabric Notebooks, Snowflake Cortex, Azure AI Foundry, AWS SageMaker, or similar), preferred
Language:
- N/A
Other Knowledge, Skills & Abilities:
- Strong ability to mentor and coach data engineers in their development and support activities, including growing their skills in AI/ML-oriented data engineering
- Ability to effectively negotiate work request priorities with business requesters and data science teams while maintaining good working relationships
- Strong analytical, organizational, and problem-solving skills
- Working knowledge of responsible AI concepts such as data privacy, bias, and appropriate handling of sensitive or client data used in AI systems
- Excellent written communication skills, maintaining accurate documentation and technical authorship of complex documents
- Ability to facilitate meetings efficiently and effectively
- Ability to follow an issue through to its logical conclusion, and escalate as necessary
- Ability to work independently as well as part of a team, including providing technical leadership to less experienced colleagues
- Excellent interpersonal and customer relationship skills
- Ability to work in a deadline-driven environment, while handling multiple complex projects/tasks simultaneously with a focus on details
- Ability to work well under pressure while dealing with unexpected issues in a professional manner
- Ability to work after hours and weekends as needed
Ability to travel as needed *
Individual salaries that are offered to a candidate are determined after consideration of numerous factors including but not limited to the candidate’s qualifications, experience, skills, and geography.
National Range: $150,000 - $175,000
Maryland Range: $150,000 - $175,000
NYC/Long Island/Westchester Range: $150,000 - $175,000
At BDO, how we show up matters. We build strong relationships by supporting one another, our clients, and our communities with care, curiosity, and a commitment to helping one another grow and succeed. Here, you’ll find meaningful work, leaders invested in your success, and opportunities to build a career around what matters most to you.
Our purpose is to be the people our clients count on to grow with confidence and achieve what matters most. Our values guide how we bring that purpose to life each day. Together, they shape how we work with one another, serve our clients, and create meaningful impact.
BDO provides assurance, tax, and advisory services to clients across the U.S. and around the world. No matter your role, you’ll be part of a team helping clients navigate complexity and move forward with clarity.
We are proud to be an ESOP company, offering participants a stake in the firm’s success through beneficial ownership and a unique opportunity to enhance their financial well-being. As a qualified retirement plan, the ESOP is a meaningful addition to our comprehensive compensation and Total Rewards benefits* offerings. It also reinforces an ownership mindset that strengthens our connection to one another, our clients, and the future we’re building together.
Learn more about our benefits: BDO Total Rewards encompass more than traditional benefits. Click here to find out more !
*Benefits may be subject to eligibility requirements.
Equal Opportunity Employer, including disability/vets
Click here to find out more !
Salary Context
This $150K-$175K range is above the median for Data Engineer roles in our dataset (median: $151K across 28 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 9,653 AI roles we're tracking, Data Engineer positions make up 1% of the market. At BDO, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($162K) sits 9% below the category median. Disclosed range: $150K to $175K.
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.
BDO AI Hiring
BDO has 2 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer. Based in US. Compensation range: $175K - $215K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national 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
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