Responsibilities
What You'll Do
We are looking for a Senior Data Engineer to ensure quick and dependable data availability for AI Proofs of Concept (POCs). This position handles collecting, processing, and providing data needed for testing—often involving synthetic, masked, or restricted datasets—to assist AI Engineers and Prompt Engineers during POC creation.
POC or equivalent experience in Data Readiness & Enablement
- Enable timely access to data needed for AI POCs, using production-aligned yet non-production data methods (synthetic, masked, sampled, or de-identified data).
- Collaborate with AI Engineers to understand data requirements that support model experimentation, timely response, and agent workflows.
- Quickly unblock data dependencies during POC or equivalent experience sprints to maintain delivery velocity.
Lightweight Data Pipelines & Integration
- Build lightweight, purpose‑specific data pipelines suitable for short‑lived POCs or equivalent experience.
- Integrate data from diverse sources (databases, files, APIs) without introducing unnecessary long‑term dependencies.
- Leverage existing enterprise tools and platforms where appropriate, while avoiding over‑engineering.
Data Quality, Safety & Governance Awareness
- Ensure POC datasets meet baseline expectations for data quality, security, and compliance.
- Partner with risk, security, and governance teams as needed to support safe experimentation.
- Clearly detail data constraints, assumptions, and limitations to inform feasibility decisions.
Collaboration with Lab Roles
- Collaborate closely with AI Engineers, Prompt Engineers, UI/UX Engineers, and Product/POC Managers to ensure alignment on data needs and trade‑offs.
- Support demo readiness by ensuring data used in POCs is representative and stable enough for collaborator evaluation.
- Distinguish data limitations vs. algorithm/model limitations during feasibility discussions.
Reusable Patterns & Lab Enablement
- Identify and detail reusable POC‑friendly data patterns (e.g., synthetic data approaches, common connectors, transformation templates).
- Contribute to improving data enablement speed across multiple POCs.
- Mentor junior engineers or adjacent team members in data approaches related to experimentation.
Qualifications
Who You Are
- Bringing a Bachelor's or Master's degree - B.E. / B.Tech / M.S. / M.Tech / MCA degree in Computer Science, Technology, Engineering, Mathematics or a related subject area.
- Over 10 years of experience in Data Engineering, Analytics Engineering, or similar positions.
- Experience operating in uncertain problem areas with partial requirements.
Technical Skills
- Solid expertise in core data engineering areas: data ingestion, transformation, and integration.
- Hands‑on experience with SQL and at least one modern data processing language or framework.
- Familiarity with cloud‑based data platforms and object storage concepts.
- Experience handling synthetic, masked, or de‑identified datasets is highly desirable.
Ways of Working
- Comfortable operating in short, time‑boxed sprint cycles with evolving data requirements.
- Pragmatic mindset—able to balance speed vs rigor and make trade‑offs explicit.
- Strong communicator who can explain data limitations and implications to non‑data collaborators.
- Demonstrated experience supporting experimentation, MVPs, or early‑stage initiatives in addition to production systems.
Skills That Will Help You Stand Out
- Experience supporting AI or analytics use cases (feature preparation, embeddings, vector data).
- Familiarity with enterprise data governance, privacy, and compliance considerations.
- Previous experience in innovation labs, incubators, or internal tooling groups.
What Success Looks Like
- POCs are not blocked due to data access or quality issues.
- AI and Prompt Engineers have the ability to rapidly test ideas using dependable, representative datasets.
- Collaborators clearly understand data readiness, constraints, and risks during go/no-go decisions.
- Reusable data patterns improve POC cycle time across the lab portfolio.
Additional Information
Why Join the AI Innovation Lab
Work across a diverse set of high‑impact AI ideas rather than a single long‑lived pipeline. Operate in a fast‑paced, learning‑first environment that values pragmatism over perfection. Directly influence how quickly AI ideas move from concept to validated proof points!
Our Engineering Culture
Within our Agile/Lean DevOps setting aimed at providing valuable solutions, we have cultivated a culture of innovation and experimentation throughout our development teams. As a customer-centered organization, we collaborate closely with our end users and product owners to grasp and quickly address evolving business requirements.
Collaboration is embedded into everything we do – from the products we develop to the quality service we provide. We're driven by the belief that diversity of thought, background, and perspective is critical to crafting the best products and experiences for our customers.
