Data Architect

Hilti•Plano, TX

About The Position

The Senior Data Architect provides technical leadership across Hilti’s data landscape, shaping enterprise data strategy and designing scalable, secure, and well-governed data platforms that power analytics, AI/ML, and intelligent applications. The role spans traditional data management—including DBMS, data modeling, warehousing, and governance—and modern cloud-native and AI data engineering on AWS, including bronze, silver, and gold patterns, RAG for LLMs, and data products for global cloud-native SaaS application teams. The architect collaborates with business stakeholders, data scientists, engineers, and platform teams to translate use cases into target data architectures, ensuring performance, reliability, security, and cost efficiency at global scale. Applicants must be legally authorized to work in the United States without current or future sponsorship.

Requirements

Bachelor’s or master’s degree in computer science, Information Systems, Data Engineering, or a related field, or equivalent experience.
10+ years designing and delivering enterprise data solutions, with hands-on experience in both traditional DBMS and cloud-native data platforms.
5+ years building on cloud platforms, with production experience in several listed AWS data and application services.
Demonstrated expertise implementing data propagation, lakehouse patterns, and governance at scale.
Proven experience supporting AI/ML workloads and delivering RAG solutions with vector search on AWS.
Strong SQL and Python skills.
Proficiency in data modeling, including IFC, 3NF, dimensional/star, or data vault approaches.
Experience with SQL Server and PostgreSQL, including schema design, indexing, query planning, replication/high availability, partitioning, and backup/restore.
Experience with Spark/EMR, Glue ETL/ELT and Studio, Airflow or Step Functions orchestration, and Parquet, Delta, or Iceberg.
Experience with MSK/Kinesis, DMS change data capture, and event-driven patterns.
Experience with IAM, KMS, Lake Formation, Secrets Manager, data masking/tokenization, and RBAC/ABAC.
Experience with Amazon Bedrock Knowledge Bases, Guardrails, or Agents; embeddings, chunking, retrieval, prompt design, token optimization, and evaluation loops.
Experience with cloud logs and metrics, lineage and quality SLAs, and storage/compute cost controls and workload rightsizing.
Experience with Git, CI/CD for data pipelines, Terraform or CDK, environment promotion, artifact/versioning, and blue/green or canary data-job deployment.
Legally authorized to work in the United States without current or future sponsorship.

Nice To Haves

Relevant certifications, such as AWS Data Analytics or AWS Solutions Architect.
Experience with Redshift RA3/Spectrum, materialized views, workload management, and federated queries.
Familiarity with Power BI, Microsoft Fabric, or Amazon Quick Sight, including semantic modeling and BI governance.
Exposure to privacy-enhancing technologies such as anonymization and differential privacy, policy as code, and guardrail frameworks.
Experience working with geographically distributed teams and regulated data.
Strategic thinking and the ability to balance short-term increments with long-term architecture.
Excellent written and verbal communication, architecture-decision facilitation, and stakeholder-alignment skills.
A mentoring mindset and demonstrated influence without authority across cross-functional teams.

Responsibilities

Architect end-to-end cloud data pipelines from raw to polished data layers using cloud-native services.
Define data storage and file-format strategies, partitioning, compaction, and indexing to optimize performance and cost.
Implement data security measures, including row- and column-level security, encryption, secrets management, and fine-grained authorization.
Design datasets and feature or knowledge layers for SageMaker training and inference and Amazon Bedrock applications.
Lead RAG architectures using vector databases, and define evaluation and feedback loops to improve answer quality.
Establish prompt, context, and token-budget standards and guide safe handling of LLM data.
Design and govern relational and NoSQL systems, including schema design, indexing, query and performance tuning, replication, partitioning, and high availability and disaster recovery.
Design and govern EDW, lakehouse, and ODS patterns and integrate them with .NET/C# services and Angular frontends through well-designed APIs and data contracts.
Define API and data-contract standards, including versioning and backward compatibility for application consumers.
Orchestrate ingestion from SAP, Salesforce, IoT/telemetry, external data sources, and partner ecosystems; standardize CDC and streaming patterns.
Partner with solution and platform architects to ensure microservices and event-driven designs are data-efficient and observability-ready.
Implement data governance, cataloging, lineage, and data quality policies, rules, thresholds, and SLAs/SLOs.
Embed monitoring and cost governance and drive continuous optimization of storage and data processing.
Enforce privacy and compliance through data minimization, tokenization, masking, and access controls.
Define secure patterns for model inputs and outputs, prompt hygiene, and guardrails to prevent data leakage or misuse.
Produce architecture blueprints, target-state designs, and build/run guardrails; guide proofs of concept and pilots.
Mentor data engineers, analysts, and junior architects and lead design reviews, threat modeling, and cost/performance reviews.
Communicate with technical and nontechnical stakeholders and promote engineering excellence and learning.