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Key Responsibilities and Required Skills for Big Data Architect

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DataBig DataArchitectureEngineeringCloud

🎯 Role Definition

The Big Data Architect is a senior technical leader who defines and implements the end‑to‑end architecture for large‑scale data platforms. This role leads the selection of technologies and patterns for data ingestion, storage, processing (batch and streaming), serving, and governance. The Big Data Architect partners with data engineering, analytics, ML teams, security, and product stakeholders to translate business objectives into scalable data solutions, ensures high availability and cost efficiency, and sets standards for code quality, data modeling, and observability.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Engineer with platform or cloud architecture responsibilities
  • Solutions Architect / Cloud Architect with a focus on data workloads
  • Analytics Engineer or ML Engineer moving into infrastructure and architecture

Advancement To:

  • Director / Head of Data Engineering
  • Chief Data Officer / Chief Data Architect
  • VP of Data & Analytics

Lateral Moves:

  • Cloud Infrastructure Architect (specializing in data)
  • Machine Learning Platform Architect
  • Data Governance / Data Privacy Lead

Core Responsibilities

Primary Functions

  • Define and document the target state big data architecture, including data lakehouse, data warehouse, streaming, and serving layers, ensuring alignment with business KPIs, regulatory requirements, and cost constraints.
  • Lead design and implementation of high‑throughput, low‑latency streaming data pipelines using Apache Kafka, Confluent, Kinesis, Pulsar or equivalent, ensuring exactly‑once or at‑least‑once semantics where required.
  • Architect and build scalable batch processing frameworks using Apache Spark, Databricks, Hadoop MapReduce, or similar, optimizing for performance, resource utilization, and fault tolerance.
  • Design data lake and lakehouse strategies (S3/ADLS/GCS backed) including partitioning, file formats (Parquet/ORC/Avro), compaction, metadata management (Hive/Glue/Unity Catalog), and catalog governance.
  • Select and implement modern data warehousing solutions (Snowflake, Redshift, BigQuery) and define ELT/ETL patterns to move trusted, curated datasets into analytics zones for BI and reporting.
  • Create robust data ingestion frameworks for structured and unstructured data sources (APIs, databases, logs, IoT), setting standards for connectors, CDC (Debezium/Glue/SCDC), throttling, and backpressure handling.
  • Define and enforce data modeling best practices (dimensional, normalized, canonical) and oversee implementation of logical and physical models that support analytics, reporting, and ML.
  • Develop and maintain architecture patterns for operationalizing ML pipelines, feature stores, model serving, and monitoring, working closely with ML engineers and data scientists.
  • Establish data security, access controls, encryption (at rest/in transit), IAM roles, tokenization, and PII handling policies in collaboration with security and compliance teams.
  • Lead capacity planning, cost estimation and optimization for cloud data platforms—right‑sizing compute, storage lifecycle policies, spot/preemptible usage, and query optimization.
  • Define and implement observability and telemetry for data platforms: logging, tracing, metrics, SLAs, SLOs, alerting, lineage and SLA dashboards using tools like Prometheus, Grafana, Datadog, or native cloud monitoring.
  • Create CI/CD and infrastructure‑as‑code pipelines (Terraform, CloudFormation, Pulumi, GitOps) for repeatable, auditable deployment of data platform components and environments.
  • Drive migration strategies from legacy ETL systems and on‑prem Hadoop clusters to cloud native architectures, including data validation, reconciliation, and rollback plans.
  • Evaluate and integrate third‑party data engineering and streaming platforms, managed services (EMR/Databricks/Managed Kafka), and SaaS analytics tools to accelerate delivery while managing vendor risk.
  • Define SLAs and runbooks for production incidents, participate in incident response, post‑mortems, and continuous improvement to reduce MTTR and improve platform reliability.
  • Collaborate with analytics, BI, and product teams to translate business requirements into data contracts, APIs, and data products that enable self‑service analytics and governed data consumption.
  • Mentor and guide data engineering teams on architecture implementations, code reviews, performance tuning, and platform best practices; establish architecture review boards or standards if needed.
  • Create and maintain architecture diagrams, standards documentation, onboarding guides, and training materials to scale knowledge across teams and stakeholders.
  • Research and introduce innovative data technologies (e.g., Delta Lake, Apache Flink, ksqlDB, Iceberg) where they solve measurable business problems, running proofs of concept and assessing tradeoffs.
  • Ensure compliance with data governance, lineage, cataloging, and metadata management requirements by designing integrated solutions across data catalogs, DQ tools, and audit logs.
  • Work with finance and procurement to establish cost allocation, tagging, and chargeback models for shared data platform resources across the organization.

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.
  • Assist in vendor evaluations and RFP responses for data platform components.
  • Review and approve technical designs and budget estimates for major data initiatives.

Required Skills & Competencies

Hard Skills (Technical)

  • Deep expertise in distributed data processing frameworks: Apache Spark, Databricks, Flink, or Hadoop ecosystem.
  • Strong experience with real‑time streaming technologies: Apache Kafka, Confluent, Kinesis, Pulsar, Kafka Streams, or ksqlDB.
  • Cloud platform proficiency: AWS (EMR, Glue, S3, Redshift, Kinesis, Athena), GCP (Dataflow, BigQuery, Pub/Sub, Dataproc), or Azure (Synapse, Event Hubs, ADLS).
  • Data lakehouse and warehousing experience: Databricks, Delta Lake, Iceberg, Snowflake, BigQuery, or Redshift Spectrum.
  • SQL mastery and performance tuning for large datasets; familiarity with query optimization and cost control.
  • Programming languages: Python, Scala, or Java for pipeline development and platform integrations.
  • Experience with CDC and ingestion tools: Debezium, AWS DMS, Kafka Connect, NiFi, or custom connectors.
  • Infrastructure as Code and orchestration: Terraform, CloudFormation, Airflow, Prefect, or Dagster.
  • Containerization and orchestration: Docker, Kubernetes; familiarity with Helm, EKS/GKE/AKS for deploying data services.
  • Data modeling, schema design, partitioning, and data partition strategy for petabyte‑scale datasets.
  • Observability and monitoring tools: Prometheus, Grafana, Datadog, ELK/EFK, AWS CloudWatch, GCP Stackdriver.
  • Security, governance, and compliance: IAM, RBAC, encryption standards, data masking, GDPR/CCPA awareness.
  • CI/CD for data pipelines and infrastructure: Git, Jenkins, GitLab CI, ArgoCD, or similar.
  • Familiarity with metadata, lineage, and data catalog tools: Apache Atlas, AWS Glue Data Catalog, Alation, or Collibra.

Soft Skills

  • Strategic thinking and ability to translate business objectives into technical roadmaps.
  • Strong communication and stakeholder management: explain complex architecture to technical and non‑technical audiences.
  • Leadership and mentoring: guide engineers, lead architecture reviews, and foster engineering best practices.
  • Problem solving under pressure and pragmatic decision making in ambiguous environments.
  • Collaboration and cross‑functional influence with product, security, finance, and analytics teams.
  • Project and vendor management skills to deliver large, multi‑team data initiatives.
  • Attention to detail and a strong orientation toward data quality and operational excellence.
  • Continuous learning mindset and curiosity about emerging data technologies.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Mathematics, Statistics, or related STEM field.

Preferred Education:

  • Master’s degree in Computer Science, Data Science, Cloud Computing, or MBA with strong technical emphasis.
  • Certifications: AWS Certified Big Data / Data Analytics, Google Professional Data Engineer, Azure Data Engineer, Databricks Certified Professional.

Relevant Fields of Study:

  • Computer Science / Software Engineering
  • Data Science / Machine Learning
  • Information Systems / Cloud Computing
  • Mathematics / Statistics

Experience Requirements

Typical Experience Range: 7–15+ years in data engineering, software engineering or cloud architecture roles with at least 3–5 years focusing on big data architecture.

Preferred:

  • Proven track record designing and delivering enterprise‑scale data platforms and migrations to cloud.
  • Experience leading cross‑functional teams, creating architecture artifacts, and influencing product roadmaps.
  • Hands‑on implementation experience with core technologies listed above and demonstrable performance and cost optimizations on production workloads.
  • Prior exposure to regulated industries (finance, healthcare, telecom) is a plus.
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