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About This Role
Description:
POSITION: Data Engineer, AI & Automation
Location & Hours: Fort Lauderdale HQ | On-site, Full-time
Department: Sound Income Strategies
FLSA Classification: Exempt
Who We Are
Sound Income Strategies, a Sound Income Group company, is a Registered Investment Adviser (RIA) with $4.5 billion in assets under management. The firm provides discretionary and non-discretionary investment management services to independent financial advisors and their clients nationwide. As a fiduciary, Sound Income Strategies is committed to portfolio oversight, regulatory compliance, and disciplined investment implementation aligned with clients’ objectives.
Position Summary
Sound Income Strategies is seeking a Data Engineer, AI & Automation to modernize our operational data infrastructure, reporting, and business workflows. Reporting directly to the Chief Technology Officer, this hands-on individual contributor will serve as the primary internal owner of backend data engineering and applied AI automation.
This role combines Snowflake expertise, strong data analysis, and production software engineering to consolidate information from custodians, trading platforms, portfolio reporting systems, and other business sources. The ideal candidate can understand how our teams work, identify valuable improvements, and independently build, deploy, and maintain reliable tools that employees use in their daily work.
Key Responsibilities
Data Engineering and Data Quality
- Develop and maintain backend data pipelines, integrations, and data models in Snowflake to support accurate, accessible operational data.
- Learn and work within the existing data aggregation architecture; integrate and reconcile information from custodians such as Schwab, trading platforms such as Orion, portfolio reporting systems such as Black Diamond, and supplemental sources.
- Analyze source data, investigate discrepancies, and implement validation, reconciliation, and quality checks to ensure reporting and downstream applications use complete, accurate information.
- Translate complex data into useful business outputs and partner with internal stakeholders and front-end contributors to validate dashboards, reports, and application functionality.
Applied AI and Workflow Automation
- Meet directly with Operations and other SIS teams to understand manual processes, define requirements, and prioritize improvements with the CTO.
- Build and deploy backend services, internal tools, and automations that improve operational efficiency and reduce repetitive manual work.
- Develop production AI functionality using Anthropic Claude, including document retrieval, text extraction, document understanding, and multi-step workflows.
- Implement and maintain Model Context Protocol (MCP) integrations connecting AI applications to approved tools and data sources, with appropriate permissions and human review.
- Use managed model platforms such as Snowflake Cortex, Amazon Bedrock, Google Vertex AI, or comparable services to support practical business applications.
Production Ownership and Collaboration
- Own solutions from requirements and design through testing, deployment, documentation, user adoption, and ongoing support.
- Monitor data pipelines and AI workflows; troubleshoot failures, evaluate output accuracy, and improve reliability, performance, and operating costs.
- Implement access controls, logging, and appropriate safeguards for sensitive client and financial data in collaboration with the CTO and Compliance.
- Participate in vendor and platform implementation meetings, learn existing systems, and coordinate technical dependencies across internal teams and external partners.
- Maintain clear technical documentation and user guidance, and communicate priorities, progress, risks, and results to technical and nontechnical stakeholders.
Requirements:
Must-Have Qualifications
- Bachelor’s degree or higher in Computer Science or a related technical discipline.
- 3+ years of software engineering experience in backend, data-intensive, or workflow automation systems, with demonstrated ability to independently build, deploy, and maintain production solutions.
- Strong hands-on proficiency in Snowflake, SQL, and a backend programming language such as Python, including experience with data modeling, APIs, and data pipelines.
- Hands-on experience integrating Claude into production business applications, including document retrieval, text extraction, and document understanding.
- Practical experience implementing Model Context Protocol (MCP) integrations and working with a managed model platform such as Snowflake Cortex, Amazon Bedrock, Google Vertex AI, or a comparable service.
- Demonstrated ownership of production LLM-powered functionality or agentic workflows, including output evaluation, failure handling, cost management, and appropriate human review.
- Experience handling sensitive data in a regulated or high-trust environment, with strong analytical, troubleshooting, and data quality skills.
- A track record of translating operational needs into practical, maintainable tools, with strong communication, documentation, collaboration, and prioritization skills.
Preferred Qualifications
- Experience in financial services preferred but not required.
- Familiarity with RIA operations, custodian data, trading systems, or portfolio reporting platforms.
- Experience with cloud-based data aggregation platforms and vendor-supported application environments.
- Ability to contribute across the application stack when a business solution requires it.
Physical & Work Environment Requirements
- This is a full-time onsite position based in Fort Lauderdale, FL – we are not considering remote candidates.
- Ability to work for extended periods at a desk using a computer.
- Ability to lift up to 10 pounds if/when necessary.
Benefits
We’re proud to offer a comprehensive benefits package that supports your professional and personal well-being, including:
- 100% employer-covered medical benefits and HRA account
- Dental & vision plans
- PTO + 10 NYSE company holidays per year
- 401K with company match program
- Free onsite parking
- Company-provided laptop and required technology
- Access to an on-site gym (free of charge)
- Weekly car wash (at an additional cost)
Sound Income Group is an equal opportunity employer that complies with all applicable federal, state, and local laws, rules and regulations. It is our policy to employ and promote qualified candidates without discrimination on the basis of race, color, sex, age, origin, sexual orientation, marital status, disability or any other characteristic protected by law. Our hiring decisions are based solely on merit, qualifications and business needs.
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 9,079 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Sound Income Group, 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 180 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Sound Income Group AI Hiring
Sound Income Group has 1 open AI role right now. They're hiring across Data Engineer. Based in Fort Lauderdale, FL, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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