Data Architect

Data Architect (Databricks) Allata is a global consulting and technology services firm with offices in the US, India, and Argentina. We help organizations accelerate growth, drive innovation, and solve complex challenges by combining strategy, design, and advanced technology. Our expertise covers defining business vision, optimizing processes, and creating engaging digital experiences. We architect and modernize secure, scalable solutions using cloud platforms and top engineering practices. Allata also empowers clients to unlock data value through analytics and visualization and leverages artificial intelligence to automate processes and enhance decision-making. Our agile, cross-functional teams work closely with clients, either integrating with their teams or providing independent guidanceto deliver measurable results and build lasting partnerships. We are looking for a Databricks Architect with strong experience in enterprise data platform architecture and governance to lead client-facing data platform implementations. This role blends high-impact architectural responsibilities (reference architectures, security, scalability, cost management, operational model) with technical leadership in designing, building, deploying, and optimizing data pipelines and data products on Lakehouse/EDW platforms (with an emphasis on Databricks). You will own the full lifecycle of data products and standardize patterns, tools, and practices across large-scale, vertical-specific implementations for our clients. Role & Responsibilities: Define the overall data platform architecture (Lakehouse/EDW), including reference patterns (Medallion, Lambda, Kappa), technology selection, and integration blueprint. Design conceptual, logical, and physical data models to support multi-tenant and vertical-specific data products; standardize logical layers (ingest/raw, staged/curated, serving). Establish data governance, metadata, cataloging (e.g., Unity Catalog), lineage, data contracts, and classification practices to support analytics and ML use cases. Define security and compliance controls: access management (RBAC/IAM), data masking, encryption (in transit/at rest), network segmentation, and audit policies. Architect scalability, high availability, disaster recovery (RPO/RTO), and capacity & cost management strategies for cloud and hybrid deployments. Lead selection and integration of platform components (Databricks, Delta Lake, Delta Live Tables, Fivetran, Azure Data Factory / Data Fabric, orchestration, monitoring/observability). Design and enforce CI/CD patterns for data artifacts (notebooks, packages, infra-as-code), including testing, automated deployments and rollback strategies. Define ingestion patterns (batch & streaming), file compacting/compaction strategies, partitioning schemes, and storage layout to optimize IO and costs. Specify observability practices: metrics, SLAs, health dashboards, structured logging, tracing, and alerting for pipelines and jobs. Act as technical authority and mentor for Data Engineering teams; perform architecture and code reviews for critical components. Collaborate with stakeholders (Data Product Owners, Security, Infrastructure, BI, ML) to translate business requirements into technical solutions and roadmap. Design, develop, test, and deploy processing modules using Spark (PySpark/Scala), Spark SQL, and database stored procedures where applicable. Build and optimize data pipelines on Databricks and complementary engines (SQL Server, Azure SQL, AWS RDS/Aurora, PostgreSQL, Oracle). Implement DevOps practices: infra-as-code, CI/CD pipelines (ingestion, transformation, tests, deployment), automated testing and version control. Troubleshoot and resolve complex data quality, performance, and availability issues; recommend and implement continuous improvements. Hard Skills - Must Have: Previous experience as architect or lead technical role on enterprise data platforms. Hands-on experience with Databricks technologies (Delta Lake, Unity Catalog, Delta Live Tables, Auto Loader, Structured Streaming). Strong expertise in Spark (PySpark and/or Scala), Spark SQL and distributed job optimization. Solid background in data warehouse and lakehouse design; practical familiarity with Medallion/Lambda/Kappa patterns. Experience integrating SaaS/ETL/connectors (e.g., Fivetran), orchestration platforms (Airflow, Azure Data Factory, Data Fabric) and ELT/ETL tooling. Experience with relational and hybrid databases: MS SQL Server, PostgreSQL, Oracle, Azure SQL, AWS RDS/Aurora or equivalents. Proficiency in CI/CD for data pipelines (Azure DevOps, GitHub Actions, Jenkins, or similar) and packaging/deployment of artifacts (.whl, containers). Experience with batch and streaming processing, file compaction, partitioning strategies and storage tuning. Good understanding of cloud security, IAM/RBAC, encryption, VPC/VNet concepts, and cloud networking. Familiarity with observability and monitoring tools (Prometheus, Grafana, Datadog, native cloud monitoring, or equivalent). Hard Skills - Nice to Have/It's a Plus: Automation experience with CICD pipelines to support deployment and integration workflows including trunk-based development using automation services such as Azure DevOps, Jenkins, Octopus. Advanced proficiency in Pyspark for advanced data processing tasks. Advance proficiency in spark workflow optimization and orchestration using tools such as Asset Bundles or DAG (Directed Acyclic Graph) orchestration. Certifications: Databricks Certified Data Engineer / Databricks Certified Professional Architect, cloud architect/data certifications (AWS/Azure/GCP). Soft Skills / Business Specific Skills: Ability to identify, troubleshoot, and resolve complex data issues effectively. Strong teamwork, communication skills and intellectual curiosity to work collaboratively and effectively with cross-functional teams. Commitment to delivering high-quality, accurate, and reliable data products solutions. Willingness to embrace new tools, technologies, and methodologies. Innovative thinker with a proactive approach to overcoming challenges.