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Key Responsibilities and Required Skills for Big Data Solution Architect

💰 $140,000 - $220,000

ITDataArchitectureCloud

🎯 Role Definition

The Big Data Solution Architect is responsible for defining, designing and delivering scalable, secure, and cost-efficient data platforms and analytics solutions that support batch and real-time processing, advanced analytics, and ML/AI workloads. This role partners with business stakeholders, data engineers, data scientists, security and operations teams to create architecture blueprints, lead proof-of-concepts, drive technical decisions, and ensure operational excellence across cloud-native and on-premise big data ecosystems.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Engineer with cloud and distributed systems experience
  • Cloud Solutions Architect with data platform exposure (AWS, Azure, GCP)
  • Big Data Engineer / Platform Engineer experienced in Hadoop, Spark or Databricks

Advancement To:

  • Principal Data Architect / Principal Big Data Architect
  • Head of Data Platform / Director of Data Engineering
  • Chief Data Officer (CDO) / VP of Data & Analytics

Lateral Moves:

  • Data Engineering Manager
  • Machine Learning Platform Architect
  • Cloud Infrastructure Architect

Core Responsibilities

Primary Functions

  • Define end-to-end big data architecture patterns and blueprints (data ingestion, storage, processing, serving, metadata, lineage, governance) aligned to business goals, security, compliance, and cost objectives across AWS/Azure/GCP and hybrid environments.
  • Lead design and implementation of scalable data lakes and data lakehouses (S3/ADLS/GCS + Delta Lake, Hudi, Iceberg), ensuring structure for both analytical and operational use cases.
  • Architect and deliver high-throughput, low-latency streaming pipelines using Apache Kafka / Confluent, AWS Kinesis, Google Pub/Sub or Azure Event Hubs and processing frameworks such as Apache Flink, Spark Structured Streaming or Kafka Streams.
  • Design and optimize batch processing jobs using Apache Spark, Hive, Presto/Trino or Databricks, including job orchestration patterns (Apache Airflow, AWS Glue, Azure Data Factory) and performance tuning for large-scale ETL/ELT.
  • Lead the migration of on-premise Hadoop ecosystems to cloud-native data platforms (Databricks, Snowflake, BigQuery, Redshift) with minimal business disruption, providing migration plans, POCs, and rollback strategies.
  • Develop data modelling and schema design strategies (star schema, normalized, wide-column) for analytical stores and OLAP systems, aligning with BI/semantic layers and downstream analytics.
  • Define metadata, catalog and data discovery strategy using tools such as AWS Glue Data Catalog, Apache Atlas, Amundsen, or Alation to improve data discoverability and lineage.
  • Establish data governance, security, and compliance controls (encryption at rest and in transit, RBAC/IAM, network controls, GDPR/CCPA considerations) and integrate them into platform architecture and CI/CD pipelines.
  • Create capacity planning, cost estimation and optimization strategies for cloud data workloads including spot/auto-scaling strategies, storage lifecycle policies, and query optimization to control cloud spend.
  • Provide technical leadership and code reviews for data engineering teams, driving best practices in distributed computing, resource isolation, dependency management and reproducible deployments.
  • Design and implement observability and operational tooling (metrics, tracing, logging, alerting) for data pipelines and platforms using Prometheus/Grafana, CloudWatch, Stackdriver, ELK and other monitoring solutions.
  • Architect secure real-time feature stores and model serving infrastructure to support production ML workflows, integrating with frameworks such as Feast, Tecton or in-house solutions.
  • Define and implement CI/CD and IaC (Infrastructure as Code) patterns for data platform provisioning and pipeline deployments using Terraform, CloudFormation, Azure ARM/Bicep, Helm and GitOps workflows.
  • Drive proof-of-concepts for emerging technologies (serverless data processing, lakehouse architectures, multi-cloud replication, parquet/ORC optimizations) and produce business case recommendations.
  • Collaborate with Data Science and Business Intelligence teams to translate analytic requirements into scalable production architectures, enabling self-service analytics and governed data access.
  • Evaluate, select and justify commercial or open-source big data technologies and partners (Databricks, Snowflake, Confluent, EMR, HDInsight, Cloudera, MapR) to meet performance, security and TCO targets.
  • Lead disaster recovery, backup and data retention architecture for large datasets (hot/warm/cold tiers), and design DR testing and runbooks.
  • Define access patterns and data APIs for analytical and transactional consumers, including performant data marts, OLAP cubes, and REST/GraphQL/gRPC APIs for downstream applications.
  • Drive cross-functional workshops, architecture governance reviews and stakeholder communication to ensure solution alignment with product roadmaps and enterprise standards.
  • Establish platform SLAs/SLOs and incident response procedures for production data systems, and lead post-incident root cause analysis and preventive action planning.
  • Mentor and build capabilities within engineering teams, create architecture reference patterns, run internal trainings and document platform best practices and design decisions.
  • Produce detailed architecture diagrams, technical specifications, non-functional requirements (scalability, availability, maintainability) and executive-level presentations for leadership and procurement.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Work with security and compliance teams to operationalize data classification and access controls.
  • Assist procurement with vendor assessments and SLA negotiations for cloud and data platform services.

Required Skills & Competencies

Hard Skills (Technical)

  • Cloud Data Platforms: Deep experience designing and operating big data architectures on AWS (S3, EMR, Glue, Redshift, Kinesis), Azure (ADLS, Databricks, Synapse, Event Hubs) and GCP (GCS, Dataflow, BigQuery, Pub/Sub).
  • Big Data Processing: Expert in Apache Spark (Scala/PySpark), Hadoop ecosystem (HDFS, YARN, Hive), and modern alternatives (Databricks, Presto/Trino).
  • Streaming & Messaging: Hands-on design and delivery with Apache Kafka/Confluent, Kafka Streams, Apache Flink, Kinesis, or similar streaming technologies.
  • Data Warehouse & Lakehouse: Architecture and optimization with Snowflake, Redshift, BigQuery, Databricks Lakehouse, Delta Lake, Hudi, Iceberg.
  • Data Modeling & ETL/ELT: Strong capabilities in logical and physical data modelling, dimensional modelling, and building robust ETL pipelines using Airflow, Glue, Data Factory or similar.
  • Databases & Storage: SQL expertise plus NoSQL and wide-column stores (Cassandra, HBase), and search/indexing systems (Elasticsearch).
  • DevOps & IaC: Experience with Terraform, CloudFormation, Jenkins, GitLab CI/CD, Kubernetes, Docker, Helm for platform provisioning and pipeline automation.
  • Security & Governance: Implementing IAM, encryption, VPC/networking, data masking, auditing, and metadata management with tools such as Apache Atlas, Ranger, or commercial equivalents.
  • Programming & Scripting: Advanced proficiency in Python, Scala or Java for data applications and automation; SQL for analytics and performance tuning.
  • Observability & Ops: Implementing logging, monitoring, tracing (Prometheus/Grafana, CloudWatch, Datadog, ELK) and incident management for data platforms.
  • Cost Optimization & Capacity Planning: Experience modeling cost for cloud data workloads and tuning for efficient use of compute and storage.
  • APIs & Integration: Designing data APIs, microservices, and integration patterns for analytics consumers (REST, gRPC, GraphQL).
  • Machine Learning Infrastructure: Familiarity with feature stores, model serving, and integration of ML pipelines into data platform architecture (MLflow, TFX, Kubeflow, Feast).

(At least 10 of the above are required for typical roles; proficiency in multiple cloud vendors and modern data platforms is strongly preferred.)

Soft Skills

  • Strong stakeholder management — ability to translate business needs into technical solutions and present trade-offs to non-technical leadership.
  • Strategic thinking — define long-term platform roadmaps and a pragmatic near-term delivery approach.
  • Communication & documentation — clear architecture diagrams, runbooks, and executive summaries.
  • Mentorship and team leadership — grow capabilities in data engineering and architect teams.
  • Problem solving and troubleshooting under production pressure.
  • Collaboration and cross-functional influence — work effectively with security, compliance, data science, and product teams.
  • Agile delivery mindset — experience operating within agile squads and iterative delivery models.
  • Vendor evaluation and negotiation skills.
  • Attention to detail and decision-making with incomplete data.
  • Continuous learning — staying current on evolving big data technologies and patterns.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, or a related technical field.

Preferred Education:

  • Master's degree in Computer Science, Data Engineering, Information Systems, or MBA with technology focus.
  • Advanced coursework or certificates in cloud architecture, data engineering, or distributed systems.

Relevant Fields of Study:

  • Computer Science
  • Software Engineering
  • Data Science / Analytics
  • Information Systems
  • Cloud Computing

Experience Requirements

Typical Experience Range: 8 - 15+ years overall technology experience with 4+ years specifically architecting large-scale big data solutions.

Preferred:

  • 10+ years designing and delivering distributed data platforms and analytics solutions.
  • 3+ years in a lead architect or senior solution architect position for cloud-based data platforms.
  • Demonstrated track record of migrating large-scale data workloads to cloud providers and optimizing for cost and performance.

Recommended Certifications (optional but advantageous): AWS Certified Data Analytics Specialty or AWS Solutions Architect Professional, Google Professional Data Engineer, Microsoft Certified: Azure Data Engineer, Databricks Certified Data Engineer, Confluent Certified Developer/Architect.

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