Senior Data Architect

We empower enterprises globally through intelligent, creative, and insightful services for data integration, data analytics and data
visualization.
Hoonartek is a leader in enterprise transformation, data engineering and an acknowledged world-class Ab Initio delivery partner.
Using centuries of cumulative experience, research and leadership, we help our clients eliminate the complexities & risk of legacy
modernization and safely deliver big data hubs, operational data integration, business intelligence, risk & compliance solutions and
traditional data warehouses & marts.
At Hoonartek, we work to ensure that our customers, partners and employees all benefit from our unstinting commitment to delivery,
quality and value. Hoonartek is increasingly the choice for customers seeking a trusted partner of vision, value and integrity
How We Work?
Define, Design and Deliver (D3) is our in-house delivery philosophy. It’s culled from agile and rapid methodologies and focused on
‘just enough design’. We embrace this philosophy in everything we do, leading to numerous client success stories and indeed to our own
success.
We embrace change, empowering and trusting our people and building long and valuable relationships with our employees, our
customers and our partners. We work flexibly, even adopting traditional/waterfall methods where circumstances demand it. At
Hoonartek, the focus is always on delivery and value.
JOB DESCRIPTION
Data Architect
GCP × Databricks | Platform & Data Architecture | Permanent / Senior IC
Function
Data & Platform Engineering
Level
Principal / Staff Architect
Employment
Permanent, Full-Time
Location
Remote-Friendly / Hybrid
Experience
8+ Years (Architecture Focus)
GCP Expertise
Professional / Expert Level
Databricks
Certified Preferred
Reports To
VP / Head of Data Platform

1. Position Summary
   We are looking for a Principal Data Architect with mastery across both Google Cloud Platform (GCP) and Databricks to lead the design
   and evolution of our enterprise data platform. This is a senior individual contributor role with broad influence — you will set the
   architectural direction for how data is ingested, stored, transformed, governed, and consumed across the organisation.
   The ideal candidate brings deep, hands-on expertise in both ecosystems — not surface-level familiarity — and has a proven track record
   of designing production-grade, scalable data platforms that serve analytics, machine learning, and operational workloads. You will work
   at the intersection of strategy and engineering, translating business requirements into robust technical blueprints while mentoring
   engineering teams on their implementation.
2. Key Responsibilities
   Platform Architecture & Design

• Define the end-to-end architecture of the enterprise data platform spanning GCP (BigQuery, Dataproc, Cloud Composer,
  Pub/Sub) and Databricks (Unity Catalog, Delta Live Tables, MLflow)
• Design and govern the lakehouse architecture — including bronze/silver/gold medallion layers, Delta Lake table design, and data
  lifecycle policies
• Architect data ingestion patterns for batch, micro-batch, and real-time streaming workloads across both platforms
• Evaluate and select tooling, frameworks, and services — balancing cost, performance, operational overhead, and strategic fit
• Produce authoritative architecture artefacts: HLDs, LLDs, data flow diagrams, decision logs (ADRs), and reference architectures
  Data Modelling & Governance
• Design logical and physical data models — dimensional, normalised, and domain-oriented — appropriate to use case and access
  pattern
• Establish and enforce data governance standards: cataloguing (Dataplex, Unity Catalog), lineage tracking, access control, and data
  classification
• Define and implement data contracts between producing and consuming teams
• Lead the adoption of data mesh or domain-oriented data ownership principles where appropriate
  Engineering Enablement & Standards
• Set engineering standards for PySpark / SQL development, pipeline design patterns, and testing practices across the data engineering
  function
• Define CI/CD practices for data pipelines — including environment promotion, schema change management, and automated testing
  gates
• Champion infrastructure-as-code (Terraform) for reproducible GCP and Databricks environment provisioning
• Establish observability standards — data quality monitoring, pipeline SLAs, alerting, and incident response runbooks
  Stakeholder & Cross-Functional Leadership
• Partner with data engineering, data science, analytics, and product teams to translate requirements into actionable architectural
  decisions
• Engage with GCP and Databricks account and technical teams to leverage roadmap features and managed support
• Provide technical oversight and architectural review on major initiatives, ensuring alignment to the target state platform
• Mentor senior engineers, conduct design reviews, and elevate architectural thinking across the data organisation

1. Required Skills & Experience
   Google Cloud Platform — Expert Level

• Deep hands-on experience designing production workloads on GCP data services: BigQuery (partitioning, clustering, BI
  Engine, materialized views), Dataproc, Cloud Composer (Airflow), Pub/Sub, Dataflow, and GCS
• Expert understanding of GCP IAM, VPC Service Controls, and security architecture for data platforms
• Experience designing multi-region, HA data architectures on GCP with DR considerations
• Proficiency with GCP cost optimisation strategies — slot reservations, storage tiers, autoscaling, and committed use discounts
• Familiarity with Vertex AI and its integration with the GCP data ecosystem for ML workloads
  Databricks — Expert Level
• Mastery of the Databricks Lakehouse Platform: Unity Catalog, Delta Lake internals, Delta Live Tables (DLT), and Photon
  engine
• Deep experience designing Databricks workspace architecture — cluster policies, job compute, SQL warehouses, and access tiers
• Expertise in Delta Lake optimisation: Z-ordering, OPTIMIZE, VACUUM, liquid clustering, and change data feed
• Hands-on experience with Databricks MLflow for experiment tracking and model registry in production
• Proficiency with Databricks Asset Bundles (DABs) or Terraform provider for IaC-based workspace management
  Core Data Architecture
• 8+ years in data engineering or data architecture roles, with at least 3 years in a dedicated architecture capacity
• Strong data modelling skills — dimensional modelling (Kimball), Data Vault 2.0, and domain-driven design applied to data
• Expert-level SQL and PySpark — ability to review, advise on, and benchmark complex transformations
• Proven experience designing real-time and streaming architectures using Kafka, Pub/Sub, or Kinesis alongside Spark Structured
  Streaming
• Deep understanding of data quality frameworks: Great Expectations, dbt tests, Soda, or equivalent
• Experience with metadata management and data cataloguing tools: Google Dataplex, Unity Catalog, Apache Atlas, or Collibra
  Certifications (Preferred)
• Google Cloud Professional Data Engineer or Professional Cloud Architect
• Databricks Certified Data Engineer Professional or Databricks Certified Associate Developer for Apache Spark
• Additional: dbt Certified Developer, AWS Solutions Architect (for polycloud exposure)

1. Technical Environment
   Google Cloud Platform Stack

• BigQuery
• Dataproc
• Cloud Composer
• Pub/Sub
• Dataflow
• GCS
• Dataplex
• Vertex AI
  Databricks Stack
• Unity Catalog
• Delta Lake
• Delta Live Tables
• MLflow
• Photon Engine
• SQL Warehouses
• Databricks Asset Bundles
• Workflows
  Cross-Platform Toolchain
• Apache Spark 3.x
• PySpark / SQL
• dbt Core / Cloud
• Great Expectations
• Terraform
• Apache Kafka
• Apache Airflow
• GitHub Actions

1. Leadership & Behavioural Competencies
   Beyond technical mastery, the successful candidate will demonstrate the following:
   Architectural Thinking
   Approaches problems from first principles; balances pragmatism with long-term platform health
   Communication
   Translates complex technical concepts clearly for engineering peers and non-technical executives alike
   Decisiveness
   Makes well-reasoned architectural decisions under ambiguity and documents them transparently via ADRs
   Mentorship
   Actively grows the architectural capability of the broader data engineering team through pairing and review
   Vendor Acumen
   Navigates GCP and Databricks roadmaps, partnerships, and commercial levers to the organisation’s advantage
   Bias for Quality
   Champions data quality, observability, and operational excellence as non-negotiable engineering standard

Thank you for your interest in this role. Please also share your CV at Vedika@lsarecruit.co.uk and if suitable, we will get in touch with you to discuss further.