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Key Responsibilities and Required Skills for Cloud Data Engineer

💰 $110,000 - $170,000

EngineeringDataCloudAnalytics

🎯 Role Definition

The Cloud Data Engineer is a senior technical role responsible for designing, building, and operating scalable, secure, and cost-efficient data platforms and pipelines in public cloud environments (AWS, GCP, Azure). This role translates business requirements into robust ETL/ELT processes, data models, and streaming solutions that power analytics, reporting, and machine learning. The ideal candidate combines deep engineering skills in Python/SQL/Spark with hands-on experience in cloud-native data services (BigQuery, Snowflake, Redshift, Databricks), orchestration tools (Airflow, Prefect), streaming platforms (Kafka, Pub/Sub), and infrastructure-as-code (Terraform, CloudFormation).

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Engineer / Data Engineer I
  • Data Analyst with strong SQL and scripting experience
  • Backend Software Engineer transitioning to data infrastructure

Advancement To:

  • Senior Cloud Data Engineer / Lead Data Engineer
  • Data Engineering Manager / Head of Data Engineering
  • Data Architect or Cloud Architect
  • Machine Learning Platform Engineer / MLE Lead

Lateral Moves:

  • Machine Learning Engineer (MLE)
  • Data Architect / DW Architect
  • Site Reliability Engineer (SRE) focused on data platforms
  • Analytics Engineering (dbt-centric) or BI Engineering

Core Responsibilities

Primary Functions

  • Design, build, and maintain scalable batch and streaming data pipelines in cloud environments (AWS, GCP, Azure) using technologies such as Apache Spark, Databricks, Airflow, Cloud Dataflow, and Kafka to reliably ingest, process, and deliver data to analytics and ML teams.
  • Architect and implement ELT/ETL workflows and transformations using SQL, Python, PySpark, dbt or Scala that convert raw source data into analytics-ready tables and data marts, ensuring reproducibility and testability.
  • Build and manage data warehouses and lakehouse solutions (e.g., BigQuery, Snowflake, Redshift, Delta Lake) and optimize storage, partitioning, clustering, and compute resources for performance and cost efficiency.
  • Implement real-time and near-real-time streaming solutions using Kafka, Kinesis, Pub/Sub, or similar platforms, including partitioning strategies, consumer groups, schema evolution, and exactly-once or at-least-once delivery semantics.
  • Create and maintain data models (star schema, normalized/denormalized) and technical metadata for downstream analytics, reporting, and machine learning use cases, collaborating closely with analytics engineers and data scientists.
  • Develop robust data ingestion patterns including CDC (Change Data Capture), API extraction, file ingestion, and database replication; handle schema drift and data quality issues proactively.
  • Implement automated testing frameworks (unit, integration, schema, and regression tests) for pipelines and transformations and integrate tests into CI/CD pipelines to ensure safe, repeatable deployments.
  • Lead the design and execution of data lineage, cataloging, and metadata management using tools such as Amundsen, Data Catalog, Alation, or built-in cloud offerings to ensure data discoverability and trust.
  • Define and enforce data governance policies, access controls, encryption standards, and compliance requirements (GDPR, CCPA, HIPAA where applicable) in collaboration with security and compliance teams.
  • Implement Infrastructure as Code (Terraform, CloudFormation, Pulumi) to provision and maintain cloud resources reliably, enable reproducible environments, and support multi-account or multi-project setups.
  • Monitor, troubleshoot, and optimize pipeline reliability, SLA adherence, query performance, and resource consumption using observability tools (Prometheus, Grafana, Cloud Monitoring, Datadog, Sentry).
  • Drive cost optimization initiatives across storage, compute, and data transfer by selecting appropriate storage tiers, autoscaling strategies, and query/cluster tuning.
  • Collaborate with product owners, data scientists, analysts, and stakeholders to translate business requirements into technical designs, prioritize work, and deliver measurable outcomes and data products (dashboards, feature stores, datasets).
  • Design and maintain secure data access layers, APIs, and data services (REST/GraphQL) for internal and external consumers, enforcing RBAC, IAM roles, and least-privilege principles.
  • Implement data partitioning, indexing, and query optimization strategies to ensure high-performance analytics and reporting for large-scale datasets.
  • Lead migrations of on-premise or legacy ETL systems to cloud-native architectures, including re-architecting batch jobs, rewriting pipelines, and validating data fidelity post-migration.
  • Build and maintain deployable Docker images, Helm charts, and Kubernetes configurations for data platform services and batch/stream processing jobs, ensuring portability and consistent runtime behavior.
  • Mentor junior data engineers and cross-functional teammates on best practices for data engineering, coding standards, pipeline observability, and incident response; conduct code reviews and provide constructive feedback.
  • Create runbooks, on-call procedures, and incident playbooks for critical data services, participate in on-call rotations, and execute root cause analysis with follow-up remediation.
  • Drive continuous improvement by researching and introducing new cloud-native data technologies (e.g., lakehouse architectures, vector databases, streaming SQL) that reduce time-to-insight and improve developer productivity.
  • Ensure data quality and reliability by implementing monitoring alerts, SLA metrics, reconciliation jobs, and automated schema validation; act as escalation point for production data incidents.
  • Collaborate with ML engineers to productionize machine learning pipelines and feature stores, ensure reproducible model training data, and support model inference/serving integrations.

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.

Required Skills & Competencies

Hard Skills (Technical)

  • Cloud Platforms: Hands-on experience with at least one cloud provider (AWS, GCP, Azure) — provisioning services, security, and cost management.
  • Data Warehousing: Expertise with BigQuery, Snowflake, Redshift, or Synapse and practical knowledge of partitioning, clustering, and query optimization.
  • Big Data Processing: Strong experience with Apache Spark, Databricks, or Hadoop ecosystems for large-scale batch processing.
  • Streaming & Messaging: Experience building streaming architectures with Kafka, Kinesis, Pub/Sub, or Flink, including schema evolution and consumer patterns.
  • Orchestration: Production experience with Airflow, Prefect, Dagster or equivalent workflow orchestration systems.
  • Programming & Scripting: Proficiency in Python and SQL; familiarity with Scala or Java a plus for distributed processing jobs.
  • ETL/ELT & Analytics Engineering: Practical use of dbt or equivalent transformation frameworks and best practices for versioned transformations.
  • Infrastructure as Code: Terraform, CloudFormation, or Pulumi for reproducible cloud infrastructure deployments.
  • CI/CD & DevOps for Data: Experience with Git, GitHub/GitLab, CI pipelines, automated testing, and deployment tooling for data projects.
  • Containerization & Orchestration: Docker and Kubernetes experience for packaging and running data services and jobs.
  • Data Modeling & Schema Design: Knowledge of dimensional modeling, normalization/denormalization, and data vault concepts.
  • Observability & Monitoring: Familiarity with Prometheus, Grafana, Datadog, Cloud Monitoring, or ELK stack for alerting and performance tracking.
  • Security & Compliance: Experience implementing IAM roles, encryption at rest/in transit, network security, and privacy controls.
  • Metadata & Cataloging: Experience implementing or integrating data catalogs, lineage tools, and metadata management solutions.
  • Performance Tuning & Cost Optimization: Proven ability to profile workloads, tune queries, and reduce cloud spending while maintaining SLAs.

Soft Skills

  • Excellent verbal and written communication skills; able to explain technical design to non-technical stakeholders and document engineering decisions.
  • Strong problem-solving mindset with an analytical approach to debugging complex data issues and system failures.
  • Collaboration and cross-functional teamwork: experience working closely with analysts, data scientists, product managers, and security teams.
  • Mentorship and leadership: ability to coach junior engineers, lead design reviews, and influence engineering best practices.
  • Time management and prioritization in a fast-paced, multi-project environment.
  • Attention to detail and commitment to data quality, observability, and operational excellence.
  • Adaptability and continuous learning mindset to keep pace with rapidly evolving cloud and data technologies.
  • Customer-focused orientation: deliver data products that solve real business problems and measure impact.
  • Strong documentation skills to create runbooks, architecture diagrams, and onboarding guides.
  • Accountability and ownership: drive end-to-end delivery and take responsibility for production systems and their reliability.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Computer Science, Data Science, Machine Learning, or a related discipline, or equivalent professional experience and certifications (e.g., AWS/GCP/Azure professional data/cloud certs).

Relevant Fields of Study:

  • Computer Science
  • Data Science / Analytics
  • Software Engineering
  • Mathematics / Statistics
  • Information Systems

Experience Requirements

Typical Experience Range: 3–7 years of hands-on data engineering experience, including at least 2+ years in cloud-based data platforms.

Preferred:

  • 5+ years building and operating production data pipelines and warehouses in one or more cloud providers.
  • Demonstrated experience with batch and streaming architectures, data modeling, ELT/ETL automation, and infrastructure-as-code.
  • Prior experience migrating legacy ETL systems to cloud-native architectures and operating data services at scale.
  • Contributions to cross-functional data products, strong track record of mentoring peers, and measurable impacts on data timeliness, quality, and cost efficiency.
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