About the Company
Blackstraw.ai is an end-to-end technology services company specializing in Artificial Intelligence (AI) and Engineering solutions across Data Science, Data Engineering, LLM/GenAI and LLMOps. Founded in 2018, we help global enterprises across North America, Europe and Asia to build and operationalize AI systems that create measurable business impact. Our mission is to make AI adoption simpler, faster and scalable through a blend of deep domain expertise, reusable accelerators and proven engineering practices.
With a 600+ strong team of engineers, data scientists and AI specialists, we partner with organizations to deliver real-world outcomes in areas such as predictive analytics, computer vision, natural language processing and Generative AI. Headquartered in Florida (USA) with operations in Canada and India, Blackstraw.ai continues to empower global enterprises to unlock the true potential of AI.
Location: USA/ Canada
Type: Fulltime only
Experience: 10 to 15 years
Role Overview
We are looking for an experienced Data Architect with deep expertise in Databricks, strong data engineering fundamentals, and a proven ability to design scalable, secure, and high-performance data platforms. The ideal candidate will have hands-on experience across modern data engineering technologies, cloud-based data architectures, and enterprise data solutions.
This role combines technical architecture and solution design with customer engagement, pre-sales support, and solution engineering. The candidate will work closely with clients, business stakeholders, sales teams, and engineering teams to understand business requirements, define technical solutions, and drive successful solution delivery.
Key Responsibilities
1. Data Architecture & Solution Design
Design end-to-end enterprise data architectures, including modern data platforms, lakehouse architectures, data warehouses, and data lakes.
Define scalable, reliable, secure, and cost-effective data solutions aligned with business and technology objectives.
Establish architectural standards, design patterns, best practices, and governance frameworks for data platforms.
Develop solution architectures covering data ingestion, transformation, orchestration, storage, processing, analytics, and consumption.
Evaluate technology options and recommend appropriate tools and platforms based on business requirements.
2. Databricks & Data Engineering
Demonstrate strong expertise in Databricks and its capabilities for enterprise data engineering and analytics.
Design and implement data pipelines using technologies such as Apache Spark, PySpark, SQL, Delta Lake, and Delta Live Tables/Lakeflow Declarative Pipelines.
Architect lakehouse solutions, including data modeling, medallion architecture, performance optimization, and workload management.
Apply best practices for data quality, data lineage, access control, security, and governance, including Unity Catalog.
Design batch and streaming data processing solutions, orchestration workflows, and scalable ETL/ELT pipelines.
Optimize data processing performance, platform utilization, and cloud infrastructure costs.
Provide technical guidance and architectural oversight to data engineers and development teams.
3. Broad Data Engineering & Cloud Expertise
Possess strong knowledge of modern data engineering concepts, distributed processing, data integration, and data modeling.
Have exposure to multiple data engineering tools, frameworks, and platforms across cloud ecosystems.
Understand cloud data architectures and services across AWS, Microsoft Azure, and/or Google Cloud Platform.
Work with technologies such as Apache Spark, Kafka, Airflow, dbt, and cloud-native data integration and orchestration services, as applicable.
Design solutions for structured, semi-structured, and unstructured data.
Understand data warehousing, dimensional modeling, data lake design, and enterprise data integration patterns.
4. Pre-Sales & Solution Engineering
Partner with sales and business development teams to understand customer needs and shape data platform opportunities.
Participate in client discussions, discovery workshops, technical presentations, and architecture assessments.
Translate business challenges into scalable technical solutions and architecture blueprints.
Develop solution proposals, technical approaches, high-level designs, effort estimates, and implementation roadmaps.
Lead or support proof of concepts (PoCs), technology demonstrations, and solution prototypes.
Present technical solutions to customer stakeholders, including engineering teams, enterprise architects, and senior leadership.
Support RFP/RFI responses, technical due diligence, and solution estimations.
Identify opportunities for modernization, migration, platform optimization, and data-driven transformation.
5. Snowflake & Data Platform Modernization
Have working knowledge of cloud data warehousing concepts and modern analytical platforms.
Exposure to Snowflake, including data architecture, data modeling, performance optimization, and integration with modern data platforms, is an advantage.
Support data platform migration and modernization initiatives involving legacy data warehouses, cloud platforms, and lakehouse architectures.
Assess existing data ecosystems and recommend improvements in scalability, performance, governance, and cost efficiency.
6. Leadership & Stakeholder Management
Collaborate with business stakeholders, product owners, data engineers, DevOps teams, and cloud architects.
Provide technical leadership and mentor engineering teams on architecture and implementation best practices.
Communicate complex technical concepts clearly to both technical and non-technical audiences.
Ensure architecture decisions align with enterprise standards, security requirements, and business outcomes.
Required Qualifications & Skills
10–18 years of overall experience in data engineering, data architecture, or enterprise data platform development.
Strong expertise in Databricks and its application in designing modern data platforms.
Strong proficiency in SQL, Apache Spark, PySpark, and data engineering design patterns.
Proven experience designing end-to-end data pipelines and scalable data architectures.
Strong understanding of lakehouse architecture, data lakes, data warehousing, and ETL/ELT processes.
Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
Knowledge of data modeling, data governance, data quality, data security, and performance optimization.
Experience in architecture design, technical solutioning, and stakeholder engagement.
Excellent communication, presentation, analytical, and problem-solving skills.
Ability to participate in pre-sales activities and translate customer requirements into technical solutions.
Preferred Qualifications
Snowflake experience is a strong plus.
Databricks certifications, such as Databricks Certified Data Engineer Professional or Databricks Certified Data Engineer Associate, are preferred.
Experience with Unity Catalog, Delta Lake, Lakeflow, and Databricks platform governance.
Exposure to streaming technologies such as Kafka and real-time data processing.
Experience with data orchestration tools such as Airflow, dbt, or equivalent technologies.
Experience in data platform migration, cloud modernization, and enterprise-scale transformation programs.
Previous experience in consulting, solution architecture, technical pre-sales, or customer-facing solution engineering.
Experience preparing technical proposals, leading PoCs, and presenting solution architectures to clients.
Ideal Candidate Profile
The ideal candidate is a hands-on, solution-oriented Data Architect who combines deep Databricks expertise with broad data engineering knowledge and strong customer-facing capabilities. They should be comfortable moving between architecture discussions, technical solution design, proof-of-concept development, and pre-sales engagements.
The candidate should be able to design the solution, articulate its business value, demonstrate technical feasibility, and guide engineering teams toward successful implementation.
Education
Blackstraw provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, national origin, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
