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Key Responsibilities and Required Skills for Data Platform Engineer

💰 $110,000 - $170,000

Data EngineeringCloudBig DataAnalyticsInfrastructure

🎯 Role Definition

The Data Platform Engineer designs, builds, and maintains scalable, secure, and cost-effective data platforms that enable analytics, reporting, and machine learning. This role focuses on cloud-native data infrastructure, data pipeline architecture, orchestration, observability, and platform automation to support robust, high-throughput data ingestion and transformation across the organization. Ideal candidates combine deep data engineering skills (SQL, Python, Spark), cloud expertise (AWS, GCP, Azure), orchestration and infrastructure-as-code experience (Airflow, dbt, Terraform, Kubernetes), and a strong emphasis on data governance, security, and operational excellence.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Engineer with hands-on ETL/ELT and pipeline experience
  • Software Engineer with backend systems and distributed systems experience
  • ETL/BI Developer transitioning to cloud-native modern data platforms

Advancement To:

  • Senior Data Platform Engineer / Lead Data Platform Engineer
  • Data Architect or Principal Data Engineer responsible for platform strategy
  • Engineering Manager or Head of Data Platform overseeing a cross-functional team

Lateral Moves:

  • Machine Learning Infrastructure Engineer
  • Site Reliability Engineer (SRE) for data services
  • Analytics Engineering or BI Engineering roles

Core Responsibilities

Primary Functions

  • Architect, design and implement end-to-end data platforms and pipeline architectures that ingest, process, and deliver petabyte-scale datasets across cloud environments (AWS, GCP, Azure) while ensuring high availability, fault tolerance, and cost efficiency.
  • Build and maintain robust ETL/ELT workflows using modern orchestration tools (Apache Airflow, Prefect, Dagster), implementing retry logic, backfills, SLA monitoring, and efficient resource utilization for scheduled and ad-hoc jobs.
  • Develop and optimize large-scale batch and streaming data processing pipelines using Spark, Databricks, Flink, or native cloud services to meet low-latency and high-throughput requirements for analytics and ML use cases.
  • Design and enforce data modeling standards, schema evolution strategies, and partitioning/clustering schemes in data warehouses and lakes (Snowflake, BigQuery, Redshift, Delta Lake) to maximize query performance and minimize cost.
  • Implement robust data ingestion patterns from diverse sources (event streams / Kafka, change data capture / Debezium, APIs, RDBMS) and ensure end-to-end data lineage, idempotency, and schema validation during ingestion.
  • Lead the build-out of a centralized data catalog, lineage, and governance capabilities (e.g., Collibra, Amundsen, Great Expectations) to improve discoverability, trust, and compliance across analytics and ML teams.
  • Develop infrastructure-as-code (IaC) for data platform components using Terraform, CloudFormation, or Pulumi to enable repeatable, auditable, and version-controlled deployments across environments.
  • Containerize data platform services and manage them on Kubernetes or managed container platforms, implementing autoscaling, resource quotas, and rolling deployments for platform reliability.
  • Collaborate with data scientists and ML engineers to provide reproducible data, feature stores, and scalable training/serving pipelines; optimize data access patterns for model training and inference.
  • Establish robust observability, monitoring, and alerting for pipelines and data platforms using Prometheus, Grafana, Datadog, CloudWatch, or Stackdriver to detect anomalies, SLA breaches, and performance regressions.
  • Implement secure data access controls, IAM roles, encryption at rest and in transit, token management, and data masking strategies in accordance with company security and compliance requirements (GDPR, CCPA, SOC2).
  • Lead incident response and root cause analysis for data platform outages, documenting remediation steps, postmortems, and long-term fixes to reduce recurrence and improve operational maturity.
  • Drive performance tuning and query optimization initiatives by analyzing query plans, indexing strategies, materialized views, and compute configurations to reduce latency and cost for analytics workloads.
  • Build reusable data platform libraries, templates, and CI/CD pipelines to standardize deployments, testing, and release processes for data infrastructure and data products.
  • Partner with product managers, analytics engineers, and business stakeholders to translate business requirements into scalable platform capabilities and prioritize roadmap items that maximize business value.
  • Design and implement data quality frameworks and automated test suites (unit, integration, contract tests) for data pipelines to prevent regressions and ensure data reliability in production.
  • Manage data lifecycle and retention policies, implementing tiered storage, compaction, and archival strategies to balance cost, performance, and compliance requirements.
  • Evaluate, prototype, and introduce new data platform technologies and patterns (lakehouse, feature stores, serverless analytics) to keep the platform modern and competitive.
  • Provide mentorship and technical leadership to junior engineers, conduct code reviews, and promote best practices in software engineering, data modeling, and platform design.
  • Create and maintain comprehensive platform documentation, runbooks, onboarding guides, and developer experience improvements to accelerate team productivity and reduce operational friction.
  • Coordinate multi-team integrations and cross-functional projects, ensuring data contracts, SLAs, and versioning are well-documented and communicated across engineering, analytics, and product teams.
  • Implement cost-optimization programs including spot/commitment discounts, compute autoscaling, and storage lifecycle policies to keep platform spend predictable and efficient.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to help analytics and product teams quickly validate hypotheses and derive insights.
  • Contribute to the organization's data strategy and roadmap, helping define long-term platform goals, migration plans, and technology selection criteria.
  • Collaborate with business units to translate data needs into engineering requirements and measurable SLAs that align with product roadmaps.
  • Participate in sprint planning and agile ceremonies within the data engineering team to ensure delivery predictability and continuous improvement.
  • Provide training sessions, brown-bags, and office hours to onboard data consumers and engineers to platform tools, best practices, and governance processes.
  • Assist in vendor evaluations and manage relationships with cloud and data technology providers to optimize licensing, support, and integration outcomes.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL and relational query optimization skills; ability to design complex analytics queries and improve performance across warehouses (Snowflake, BigQuery, Redshift).
  • Strong Python and/or Scala programming ability for data pipeline development, ETL frameworks, and automation scripts.
  • Distributed data processing expertise with Apache Spark, Databricks, Flink, or equivalent frameworks for batch and streaming workloads.
  • Experience designing and operating streaming architectures and event platforms using Apache Kafka, Kinesis, or Pub/Sub, including schema registries and consumer scaling patterns.
  • Hands-on knowledge of orchestration and workflow tools such as Apache Airflow, Prefect, or Dagster for scheduling, monitoring, and dependency management.
  • Proficiency with cloud platforms (AWS, GCP, Azure) and managed data services (e.g., Redshift, BigQuery, Snowflake, EMR, Dataflow, Databricks).
  • Infrastructure-as-code and automation skills using Terraform, CloudFormation, or Pulumi to provision and manage platform resources reliably.
  • Containerization and orchestration experience with Docker and Kubernetes for packaging and deploying data services and microservices.
  • Familiarity with analytics engineering tools such as dbt (data build tool) for modular transformations, testing, and documentation of analytics models.
  • Strong understanding of data modeling, dimensional modeling, and best practices for designing subject-area schemas, star/snowflake schemas, and data marts.
  • Experience with data governance, metadata management, lineage tooling, and data quality frameworks (Great Expectations, Deequ, Monte Carlo).
  • CI/CD, testing, and release management experience for data pipelines and infrastructure, including GitOps workflows and automated deployment/testing pipelines.
  • Security and compliance knowledge: IAM, VPCs, encryption, network policies, and experience implementing role-based access controls and data redaction practices.
  • Monitoring, observability, and alerting skills using Prometheus, Grafana, Datadog, CloudWatch, or equivalent platforms with an emphasis on SLA tracking and anomaly detection.
  • Performance tuning, cost optimization, and capacity planning for data platform workloads, including query profiling, partitioning, and compute sizing.

(At least 10 of the above should be considered required technical skills for hiring and screening.)

Soft Skills

  • Strong collaboration and stakeholder management skills to align technical delivery with business outcomes and partner effectively across analytics, product, and security teams.
  • Excellent written and verbal communication; able to document architecture, produce runbooks, and explain complex technical concepts to non-technical audiences.
  • Analytical and systems thinking mindset with attention to detail when troubleshooting, performing root cause analysis, and designing resilient systems.
  • Proactive, self-directed problem solver who can prioritize work in a fast-moving environment and drive projects from concept to production.
  • Mentorship and leadership capability to upskill peers, promote best practices, and foster a culture of engineering excellence and continuous improvement.
  • Adaptability and learning agility to evaluate and adopt new technologies, patterns, and approaches as platform needs evolve.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Computer Science, Data Science, Systems Engineering, or an MBA with strong technical experience.
  • Relevant professional certifications (e.g., AWS Certified Data Analytics, Google Professional Data Engineer, Snowflake SnowPro, Databricks Certification).

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 3–8 years of professional experience in data engineering, platform engineering, or related roles with demonstrable ownership of production data systems.

Preferred: 5+ years building and operating cloud-native data platforms, experience with multiple cloud providers and large-scale ETL/streaming systems, a track record of architecting data solutions that power analytics and ML at scale.

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