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Key Responsibilities and Required Skills for AWS Data Engineer

💰 $100,000 - $160,000

EngineeringDataCloudAWS

🎯 Role Definition

The AWS Data Engineer is responsible for designing, building, and operating scalable, secure, and cost-effective data platforms on AWS. This role focuses on modern data architecture including data lakes, data warehouses, ETL/ELT pipelines, streaming ingestion, and automation using AWS native services (S3, Glue, Redshift, Athena, Kinesis, Lambda, EMR) and orchestration tools (Airflow/MWAA). The ideal candidate combines strong software engineering skills, deep knowledge of big data technologies (Spark, Kafka), infrastructure-as-code (Terraform/CloudFormation), and a pragmatic approach to data quality, security, and observability.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning to engineering responsibilities with hands-on ETL experience and strong SQL skills.
  • ETL/BI Developer with experience building batch jobs and data pipelines on-prem or in cloud environments.
  • Software Engineer with interest in data systems and distributed processing (Spark, Python, Scala).

Advancement To:

  • Senior Data Engineer — owning cross-team data platform initiatives and complex architecture decisions.
  • Principal/Staff Data Engineer — technical leader responsible for data platform strategy and best practices.
  • Data Engineering Manager / Director of Data Engineering — team leadership, budget and roadmap ownership.
  • Cloud Solutions Architect / Data Architect — designing enterprise-level data solutions and governance.

Lateral Moves:

  • Data Scientist — leveraging deep platform knowledge to build advanced ML pipelines and feature stores.
  • Cloud Engineer / DevOps Engineer — specializing in cloud infra, IaC, and platform automation.

Core Responsibilities

Primary Functions

  • Design, build, and maintain robust ETL/ELT data pipelines on AWS using services such as AWS Glue, AWS Lambda, AWS Step Functions, and Amazon EMR to ingest, transform, and load structured and unstructured data at scale.
  • Architect and implement a secure, performant data lake on Amazon S3 including lifecycle policies, partitioning strategies, and access controls with AWS Lake Formation or Glue Data Catalog for discoverability and governance.
  • Develop and optimize data warehouse solutions (Amazon Redshift, Redshift Spectrum, or Snowflake on AWS) including schema design, distribution keys, sort keys, vacuuming strategies, and workload management to ensure low-latency analytics.
  • Build real-time and near-real-time streaming data pipelines using Amazon Kinesis, Kafka (MSK), or other streaming platforms, integrating with processing frameworks like Apache Flink or Spark Structured Streaming to support event-driven analytics.
  • Author and tune high-performance Spark jobs (PySpark/Scala) on EMR or Glue to transform multi-terabyte datasets, focusing on memory management, partitioning, and shuffle optimization.
  • Implement orchestration and scheduling of data workflows using Apache Airflow, Managed Workflows for Apache Airflow (MWAA), or Step Functions, with robust retry logic, SLA monitoring, and lineage tracking.
  • Create and maintain Infrastructure-as-Code (IaC) using Terraform or AWS CloudFormation to provision repeatable, auditable data infrastructure including VPCs, IAM roles, S3 buckets, and Redshift clusters.
  • Design and enforce data quality frameworks and automated validation tests (unit tests, integration tests, data schema checks, row-count validations) integrated into CI/CD pipelines to prevent data regressions.
  • Build CI/CD pipelines for data engineering (code commits, automated testing, container builds, deployment to staging and production) using GitHub Actions, Jenkins, CodePipeline or similar tools.
  • Implement secure data access patterns including fine-grained IAM policies, encryption at rest and in transit (KMS), S3 bucket policies, VPC endpoints, and role-based access for analytics consumers.
  • Instrument pipelines and data platforms with monitoring and observability (CloudWatch, Datadog, Prometheus/Grafana, AWS X-Ray) to provide alerting, performance metrics, cost metrics, and debugability for production systems.
  • Perform cost optimization analyses and apply cost-control mechanisms such as compute autoscaling, spot instances for EMR, Redshift Concurrency Scaling, and S3 intelligent-tiering.
  • Collaborate with data consumers (analytics, BI, ML teams) to design efficient data models, materialized views, tables, and APIs that meet SLAs and business requirements.
  • Develop metadata management and data cataloging processes using Glue Data Catalog, Data Catalog integrations, or 3rd-party tools to support discoverability, lineage and compliance needs.
  • Implement connectors and ingestion patterns for on-prem, SaaS, and API-based data sources (JDBC, REST, FTP, Snowflake, third-party APIs) and manage incremental/CDC ingestion strategies.
  • Troubleshoot production incidents, conduct root cause analysis, write postmortems, and implement preventive measures and runbooks to improve reliability and reduce MTTR.
  • Partner with security, compliance, and governance teams to ensure data handling adheres to regulatory requirements (GDPR, HIPAA, PCI) and company policies by designing classification, masking, and retention strategies.
  • Mentor and coach junior data engineers, conduct code reviews, evangelize best practices for coding standards, testing, and documentation across the team.
  • Lead proofs-of-concept and prototype initiatives for new AWS services (e.g., Glue Elastic Views, Athena improvements) and big data patterns to inform platform roadmap and technology choices.
  • Maintain thorough technical documentation and runbooks for data pipelines, schemas, operational playbooks, and onboarding guides to facilitate cross-team collaboration and knowledge transfer.
  • Evaluate and integrate analytics tools and BI platforms (Amazon QuickSight, Tableau, Looker) to deliver self-service analytics capabilities and reliable datasets for business reporting.
  • Design and deploy feature engineering pipelines and batch/streaming data feeds to support machine learning platforms and MLOps workflows, ensuring reproducibility and lineage.
  • Ensure data ingest and transformation performance targets by designing efficient partitioning strategies, compression settings (Parquet/ORC), and predicate pushdown for query performance.
  • Participate in architecture and design reviews, provide estimation and technical input for planning cycles, and influence roadmap prioritization for data platform investments.

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)

  • AWS Core Services: Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, AWS Lambda, Amazon EMR, AWS Kinesis, Amazon MSK, and AWS Step Functions.
  • Big Data Processing: Strong experience with Apache Spark (PySpark/Scala), EMR, Glue ETL jobs, optimizing distributed compute workloads.
  • Databases & Warehouses: Deep SQL expertise and experience with columnar warehouses (Redshift, Snowflake) and OLAP data models.
  • Streaming & Messaging: Design and operate streaming pipelines using Kinesis, Kafka/MSK, or similar platforms; experience with event-driven architectures.
  • Orchestration: Hands-on with Apache Airflow or MWAA, Step Functions, DAG design, SLA/alerting and retry strategies.
  • Programming & Scripting: Python (preferred), Scala, and shell scripting for automation; strong unit/integration test practices.
  • Infrastructure as Code: Terraform and/or AWS CloudFormation for provisioning and managing data infrastructure and IAM policies.
  • CI/CD & DevOps: Experience implementing CI/CD pipelines for data engineering, containerization (Docker), and build automation.
  • Data Modeling & ETL Patterns: Dimensional modeling, star/snowflake schemas, CDC patterns, partitioning, compression and file formats (Parquet/ORC/Avro).
  • Data Security & Governance: KMS, IAM, encryption best practices, data masking and implementation of least-privilege access controls.
  • Observability & Monitoring: CloudWatch, Datadog, Prometheus/Grafana, distributed tracing and logging for pipeline health and alerting.
  • Performance Tuning & Cost Optimization: Query optimization, resource sizing, spot/auto scaling strategies and cost analysis on AWS.
  • Metadata & Cataloging: AWS Glue Data Catalog, AWS Lake Formation, or equivalent metadata/catalog tools for lineage and discoverability.
  • SQL & Analytical Tools: Strong SQL skills, familiarity with BI tools (QuickSight, Tableau, Looker) and building reliable data marts.
  • Message Formats & APIs: JSON, Avro, Protobuf, RESTful API integrations and connectors (JDBC, S3 ingestion, API polling).

(At least 10 of the above should be present and emphasized when screening candidates.)

Soft Skills

  • Strong verbal and written communication: translate technical details to non-technical stakeholders and write clear runbooks and documentation.
  • Problem-solving and analytical mindset: debug complex pipelines and make data-driven trade-offs.
  • Collaboration and stakeholder management: work cross-functionally with analytics, ML, product, and security teams.
  • Ownership and accountability: drive projects end-to-end and operate reliably in production environments.
  • Mentorship and team leadership: coach junior engineers, perform constructive code reviews and nurture best practices.
  • Prioritization and time management: balance urgent production issues with long-term platform improvements.
  • Adaptability and continuous learning: stay current with evolving AWS services, big data tools, and architectural patterns.
  • Attention to detail and data quality focus: enforce schema contracts, testing, and validation to maintain trust in datasets.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Computer Science, Data Engineering, or related disciplines, or specialized cloud/data engineering certifications (AWS Certified Data Analytics – Specialty, AWS Certified Solutions Architect).

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 3–8 years of professional experience building and operating data pipelines, with at least 2+ years focused on AWS cloud data services.

Preferred:

  • 5+ years in data engineering or related roles with demonstrable ownership of end-to-end data platforms on AWS.
  • Proven track record of building production-grade ETL/ELT workflows, streaming pipelines, and data warehouses.
  • Hands-on experience leading small teams or mentoring engineers, performing architecture design, and driving platform improvements.
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