Senior Translational Data & AI Engineer

Job Title: Senior Data & AI Engineer

Job Description

The Senior Data & AI Engineer plays a key role in driving scientific data engineering initiatives across the research pipeline, transforming complex multi-modal scientific data into reliable, actionable assets. This is a hands-on technical position focused on building modern, AI-native data platforms, onboarding multiple clinical and biological studies, and modernizing data engineering practices in a cloud environment.

Responsibilities

Drive scientific data engineering initiatives across the research pipeline by designing and implementing robust data platforms that support clinical, biological, and multi-modal research data.

Collaborate with IT, computational biology, and translational science leads to transform raw genomics, proteomics, and other assay data into curated, analysis-ready datasets.

Build and maintain orchestrated ingestion pipelines for external genomics, proteomics, and other assay data sources, including source input/output, table-format writers, and row-level reconciliation.

Develop and harden layered transformation models (staging, intermediate, and data marts) with comprehensive real-data test coverage, strong data-quality guardrails, and reusable, consolidated transformation logic.

Implement clinical data ingestion and reconciliation workflows aligned with recognized industry standards such as SDTM and ADaM, including subject and entity resolution.

Deliver and maintain supporting platform infrastructure, including service APIs, CI/CD pipelines, containerized deployments, observability and monitoring instrumentation, and data-warehouse performance tuning.

Extract transformation logic and business rules from legacy analytical codebases (such as R or PySpark) and reconcile them with new platform implementations to ensure consistency and correctness.

Translate scientific, biomarker, and bioinformatics requirements into durable data models and clearly defined, published data contracts that can be reused across teams.

Identify repetitive or manual processes and convert them into automated workflows, guardrails, or reusable tooling, including AI-assisted workflows that accelerate future development.

Leverage AI coding agents and tooling to build systems and workflows around AI, rather than using them only for ad hoc prompting, and ensure appropriate guardrails around AI-generated output.

Participate in design and code reviews with an adversarial mindset, identifying edge cases, surfacing potential failure modes, and challenging suboptimal patterns to improve overall system quality.

Contribute to onboarding multiple new scientific studies by focusing on either data ingestion or data visualization workflows, helping scale the platform to support 10+ additional studies.

Collaborate closely with teammates to divide work effectively so that each engineer can independently own and deliver well-defined components of the data platform.

Continuously modernize data engineering and AI practices by adopting up-to-date tools, frameworks, and methodologies in cloud-based data and AI environments.

Essential Skills

Demonstrated AI-native engineering practice, with hands-on experience building systems and workflows around AI coding agents (such as GitHub Copilot, Cursor, Codex, or equivalent), beyond simple prompting.

Ability to recognize when repeated processes should be converted into automated pipelines and when AI agent output requires guardrails or additional infrastructure to ensure reliability.

Bachelor's or master's degree in Computer Science, Data Engineering, Bioinformatics, or a closely related field.

At least 4+ years of professional experience in data engineering with shipped production data pipelines on AWS, including services such as S3, ECS or Fargate, and Redshift or an equivalent massively parallel processing (MPP) data warehouse.

Strong proficiency in Python for data engineering, including building data pipelines, transformations, and automation scripts.

Strong proficiency in SQL, including writing complex queries and working with large-scale analytical databases or data warehouses.

Working knowledge of modern data engineering libraries and frameworks used for building scalable ingestion and transformation pipelines.

Hands-on experience with data lake and data warehouse architectures, including designing and operating data lakehouse or similar patterns.

Familiarity with AI tooling and the ability to effectively navigate and integrate new AI tools into engineering workflows, with the understanding that this is critical to success in the role.

Solid understanding of core data engineering principles, including data modeling, ETL/ELT design, data quality, and reliability.

Ability to understand and work with clinical and biological (bio/omics) data, including the fundamentals of how such data is structured and used in research.

Experience implementing clinical data ingestion and reconciliation processes aligned with standards such as SDTM and ADaM.

Additional Skills & Qualifications

Experience extracting and refactoring transformation logic and business rules from legacy analytical codebases written in languages such as R or PySpark.

Background or exposure to bioinformatics, computational biology, or related scientific domains, particularly involving genomics, proteomics, or multi-modal assay data.

Experience designing and implementing layered transformation models (staging, intermediate, mart) with strong test coverage and data-quality checks.

Hands-on experience delivering platform infrastructure such as service APIs, CI/CD pipelines, containerized deployments, and observability tooling.

Familiarity with subject and entity resolution techniques in clinical data pipelines.

Experience with adversarial or rigorous design and code review practices, including identifying edge cases and challenging design decisions to improve robustness.

Interest in modernizing data engineering practices and staying up to date with emerging tools and methodologies in data and AI.

Ability to work independently on a defined piece of the data platform, such as data ingestion or data visualization, while collaborating effectively with a broader team.

Work Environment

This role is fully remote, with a preference for candidates located in the Eastern time zone, although Central time zone is also suitable. You will work in a modern cloud-based environment centered on AWS services such as S3, ECS or Fargate, and Redshift or equivalent MPP data warehouses. The engineering culture emphasizes AI-native development practices, including the use of AI coding agents and automation to accelerate workflows. You will collaborate closely with IT, computational biology, and translational science teams in a distributed setting, using contemporary collaboration, version control, and CI/CD tools. The environment focuses on modernizing data engineering capabilities, scaling to support multiple concurrent scientific studies, and fostering thoughtful code reviews, robust data quality practices, and continuous improvement.

Job Type & Location

This is a Contract position based out of Wilmington, DE.

Pay and Benefits

The pay range for this position is $74.00 - $78.00/hr.

Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to specific elections, plan, or program terms. If eligible, the benefits available for this temporary role may include the following: - Medical, dental & vision - Critical Illness, Accident, and Hospital - 401(k) Retirement Plan - Pre-tax and Roth post-tax contributions available - Life Insurance (Voluntary Life & AD&D for the employee and dependents) - Short and long-term disability - Health Spending Account (HSA) - Transportation benefits - Employee Assistance Program - Time Off/Leave (PTO, Vacation or Sick Leave)

Workplace Type

This is a fully remote position.

Application Deadline

This position is anticipated to close on Aug 10, 2026.

About Actalent

Actalent is a global leader in engineering and sciences services and talent solutions. We help visionary companies advance their engineering and science initiatives through access to specialized experts who drive scale, innovation and speed to market. With a network of almost 20,000 consultants and 5,000 clients across the U.S., Canada, Asia and Europe, Actalent serves many of the Fortune 500. We are proud to be an Engineering News-Record (ENR) Top 500 Design Firm for our engineering design services and a ClearlyRated Best of Staffing® winner for both client and talent service.

The company is an equal opportunity employer and will consider all applications without regard to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.

If you would like to request a reasonable accommodation, such as the modification or adjustment of the job application process or interviewing process due to a disability, please email actalentaccommodation@actalentservices.com for other accommodation options.

San Francisco Fair Chance Ordinance: Pursuant to the San Francisco Fair Chance Ordinance, for all positions located in the city and county of San Francisco, we will consider for employment qualified applicants with arrest and conviction records.

Massachusetts Lie Detector: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Use of Artificial Intelligence (AI): We may use Artificial Intelligence (AI) to support parts of our hiring process, including sourcing, screening, and evaluating candidates. AI helps assess applications and qualifications, but final decisions are made by our hiring team. By applying, you acknowledge and agree that your application may be reviewed using AI tools.