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Databricks Architect information

What is a Databricks Architect?

A Databricks Architect is an IT professional who designs, implements, and manages data solutions using the Databricks platform, which is built on Apache Spark. They are responsible for creating scalable data pipelines, optimizing data workflows, and ensuring security and compliance within the cloud environment. Databricks Architects often work closely with data engineers, data scientists, and business stakeholders to deliver robust analytics solutions that drive business insights. Their expertise helps organizations leverage big data technologies efficiently and effectively.

What are the key skills and qualifications needed to thrive as a Databricks Architect?

To thrive as a Databricks Architect, you need strong expertise in big data engineering, cloud platforms (such as Azure or AWS), distributed computing, and proficiency in languages like Python or Scala, typically supported by a relevant degree and cloud certifications. Familiarity with Databricks Workspace, Apache Spark, Delta Lake, and CI/CD tools is crucial for designing and implementing scalable data solutions. Excellent problem-solving, communication, and project management skills set top performers apart by enabling effective collaboration and solution delivery. These competencies are essential for architecting reliable, high-performance data platforms that drive business insights and innovation.

What are some common challenges Databricks Architects face when designing large-scale data solutions?

Databricks Architects often encounter challenges such as optimizing cluster performance for cost and efficiency, ensuring data security and compliance across distributed environments, and integrating Databricks with legacy systems or diverse data sources. They must carefully design data pipelines and workflows to handle large volumes of data without bottlenecks, and also collaborate closely with data engineers, data scientists, and IT teams to align on best practices. Staying updated with evolving Databricks features and cloud platform updates is also essential for success in this dynamic role.

What is the difference between Databricks Architect vs Data Engineer?

AspectDatabricks ArchitectData Engineer
Primary FocusDesigning and implementing data solutions on Databricks platformBuilding, maintaining, and optimizing data pipelines and infrastructure
Skills & CertificationsDatabricks certifications, Spark, cloud platforms (AWS, Azure), SQLSQL, ETL tools, cloud platforms, programming (Python, Scala)
Work EnvironmentData platforms, cloud environments, collaboration with data teamsData pipelines, databases, cloud infrastructure, scripting

While both roles work with data and cloud platforms, a Databricks Architect primarily focuses on designing and implementing data solutions using Databricks, whereas a Data Engineer builds and maintains the data pipelines and infrastructure that support these solutions. The Architect often oversees the technical design, while the Engineer handles the day-to-day pipeline development.

What are popular job titles related to Databricks Architect jobs in Boston, MA?

For Databricks Architect jobs in Boston, MA, the most frequently searched job titles are:

What job categories do people searching Databricks Architect jobs in Boston, MA look for?

The top searched job categories for Databricks Architect jobs in Boston, MA are:

What cities near Boston, MA are hiring for Databricks Architect jobs?

Cities near Boston, MA with the most Databricks Architect job openings:

Databricks Architect jobs near you
Infographic showing various Databricks Architect job openings in Boston, MA as of August 2026, with employment types broken down into 87% Full Time, 4% Part Time, and 9% Contract. Highlights an 82% Physical, 6% Hybrid, and 12% Remote job distribution.

Databricks Architect

Tata Consultancy Service Limited

Marlborough, MA • On-site

$110K - $140K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 9 days ago

Job description

Must Have Technical/Functional Skills
  • Experience: 10+ years of hands-on data engineering experience, with at least 3 years focused on the Databricks/Spark
  • Ecosystem
  • Databricks Expertise: Deep, hands-on expertise with the Databricks Lakehouse Platform, including Delta Lake,
  • Structured Streaming, Lakeflow Declarative Pipelines (formerly Delta Live Tables), Databricks SQL, and cluster/serverless configuration and optimization.
  • Business Semantics & Governance: Hands-on experience with Unity Catalog Business Semantics, including Metric Views (measures, dimensions, materialization), Domains, and Pages/Glossary to define governed, reusable KPIs once and serve them consistently across SQL, BI, and AI agents.
  • Agentic & Conversational Analytics: Working knowledge/exposure of AI/BI Genie (Genie Spaces, Genie Ontology, trusted assets), Genie Code for agentic pipeline/SQL development, Databricks One for business-user consumption, and Mosaic AI for building and serving AI/ML models and agents.
  • Programming Mastery: Expert-level proficiency in Python and PySpark. Advanced SQL skills are essential.
  • Data Warehousing Concepts: Strong understanding of data modeling principles, including dimensional modeling
  • (Kimball), data warehousing concepts, and ETL/ELT design patterns.
  • Cloud Proficiency: Proven experience working with a major cloud provider (Azure, AWS, or GCP), particularly with
  • data storage S3 and related services.
  • Software Engineering Mindset: Experience with software engineering best practices, including version control (Git),
  • code reviews, testing, and CI/CD.
  • Certification: Databricks

Roles & Responsibilities
  • Data Pipeline Development: Design, code, and deploy robust and scalable batch and streaming data pipelines
  • using PySpark, Spark SQL, and Lakeflow (Lakeflow Connect for ingestion and Lakeflow Declarative Pipelines) to ingest data from sources such as Point-of-Sale (POS), e-commerce platforms, loyalty systems, and marketing clouds.
  • Data Modeling & Transformation: Implement complex data transformations and business logic within the Medallion
  • architecture (Bronze, Silver, Gold layers). Build and optimize the final "Gold" dimension tables that will
  • serve as the single source of truth. Define governed business KPIs on top of the Gold layer using Unity Catalog Metric Views (business semantics) so metrics are computed once and reused consistently across BI, SQL, and Genie.
  • Data Quality: Implement data quality frameworks and cleansing routines to ensure the accuracy and trustworthiness
  • of the Customer 360 data.
  • Performance Optimization: Proactively monitor, debug, and tune Databricks jobs and Spark clusters for performance
  • and cost-efficiency. Implement best practices for partitioning, caching, and data layout in Delta Lake.
  • Infrastructure as Code (IaC) & CI/CD: Work with DevOps teams to manage Databricks environments, clusters, and
  • job deployments using tools like Terraform and AWS DevOps/GitHub Actions. Champion and implement CI/CD best
  • practices for data pipelines.
  • Data Governance & Security: Implement data governance features within Databricks Unity Catalog, including
  • data lineage tracking, fine-grained access controls (row/column-level security and ABAC policies), tags, and data masking to ensure compliance and security across BI and AI/agent workloads.
  • C ollaboration: Partner closely with Functional Consultants, Data Scientists, and Analytics Engineers to understand
  • their data requirements and deliver well-structured, consumption-ready datasets.
  • Semantic Modeling & Business Semantics: Build and govern Unity Catalog Business Semantics, authoring Metric Views (measures, dimensions, joins, synonyms, materialization), Domains, Pages, and Glossary terms, and certifying trusted assets so every dashboard, SQL query, notebook, and AI agent works from the same governed definitions.
  • Genie & Agentic Analytics Enablement: Configure and curate AI/BI Genie Spaces and the Genie Ontology (instructions, trusted assets, example queries, synonyms) to deliver accurate natural-language, conversational analytics for business users, and enable consumption through AI/BI Dashboards and Databricks One.

TCS Employee Benefits Summary:
  • Discretionary Annual Incentive.
  • Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
  • Family Support: Maternal & Parental Leaves.
  • Insurance Options: Aut& Home Insurance, Identity Theft Protection.
  • Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
  • Time Off: Vacation, Time Off, Sick Leave & Holidays.
  • Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.

#LI-KR3
Salary Range-$110,000-$140,000 a year

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