Key Responsibilities and Required Skills for Jade Carver

🎯 Role Definition

Jade Carver is a senior-level data engineering role focused on designing, building, and operating scalable, reliable data pipelines and analytics platforms. The role partners closely with data scientists, product owners, and business stakeholders to translate analytical needs into production-ready data solutions, championing best practices for data quality, governance, and performance in cloud-native environments.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Engineer (Mid-level) with 2–4 years of production pipeline experience.
Software Engineer with experience in backend systems, ETL, or distributed systems.
Business Intelligence Engineer or Analytics Engineer responsible for orchestrating data flows and reports.

Advancement To:

Senior Data Engineering Lead / Principal Data Engineer
Data Engineering Manager or Head of Data Platform
Staff/Principal Engineer focused on data infrastructure and architecture

Lateral Moves:

Machine Learning Engineer (in collaboration with ML teams)
Analytics Engineering / BI Lead
Site Reliability Engineer for Data Platforms

Core Responsibilities

Primary Functions

Design, implement, and maintain end-to-end data pipelines that ingest, process, and deliver terabytes of data daily using modern ETL/ELT patterns to support analytics, reporting, and machine learning use cases.
Build and optimize scalable batch and real-time data architectures using cloud services (e.g., AWS/GCP/Azure), stream processing frameworks, and distributed compute engines to meet latency and throughput SLAs.
Author robust, well-tested data transformation logic using SQL, Python, Spark, or similar technologies and ensure transformations are modular, documented, and performant for downstream consumers.
Implement and operate production-grade orchestration workflows (Airflow, Prefect, Dagster, or equivalent) including scheduling, retry strategies, monitoring, and alerting to maintain pipeline reliability.
Collaborate with data scientists and machine learning engineers to productionize feature pipelines, model inference endpoints, and metrics generation for ML lifecycle management.
Develop and enforce data quality checks, validations, and anomaly detection using automated testing frameworks and observability tools to minimize data regressions and ensure trust in analytics.
Design and maintain data storage solutions (data lake, data warehouse, OLAP) including partitioning, compaction, schema design, and lifecycle policies to control cost and increase query performance.
Create and maintain comprehensive documentation, runbooks, and onboarding materials for dataset schemas, pipeline contracts, data lineage, and operational procedures.
Lead technical design discussions and architecture reviews to drive consistent standards across data platforms, refactoring legacy ETL into scalable, maintainable solutions.
Implement data governance practices, access controls, and encryption strategies to ensure compliance with company policies and regulatory requirements such as GDPR, CCPA, or HIPAA where applicable.
Monitor pipeline health and performance metrics, proactively triage incidents, conduct post-incident analyses, and implement remediation to prevent future outages.
Mentor junior engineers, conduct code reviews, and promote engineering best practices such as version control, CI/CD for data pipelines, and reproducible environments.
Partner with product and business stakeholders to translate analytical questions into data requirements, define success metrics, and prioritize roadmap initiatives based on business impact.
Develop cost-optimization strategies across compute and storage, including right-sizing clusters, scheduling non-peak workloads, and selecting appropriate storage classes.
Integrate third-party data sources and APIs, performing robust ingestion, transformation, schema alignment, and reconciliation to ensure consistent master datasets.
Implement schema evolution strategies, contract testing, and backward compatibility approaches to minimize consumer breakage during data model changes.
Create automated deployment pipelines for infrastructure-as-code (Terraform, CloudFormation, Pulumi) to provision and manage data platform resources reliably and reproducibly.
Drive initiatives to improve query performance and user experience for analysts by implementing materialized views, aggregated tables, and tuned indexes where appropriate.
Evaluate, pilot, and adopt new data tooling and platforms that accelerate engineering productivity, improve reliability, or reduce total cost of ownership.
Conduct capacity planning and scaling exercises for data infrastructure to meet anticipated business growth and seasonal traffic patterns.
Manage cross-functional projects impacting multiple teams, ensuring timely delivery through clear requirements, milestone tracking, and risk mitigation.
Ensure consistent metadata capture and lineage tracking to support auditability, impact analysis, and discoverability across datasets and pipelines.
Champion best practices for observability and telemetry across data systems, including metrics, logs, and distributed tracing where applicable.
Coordinate with security and compliance teams to perform data access reviews, audits, and remediate any discovered issues related to data exposure.
Lead optimization and refactoring of legacy code and pipelines to reduce technical debt and increase maintainability and reliability.

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)

Expert SQL: advanced query tuning, window functions, CTEs, and performance optimization for large datasets.
Python (or Scala/Java): writing production-grade data transformations, libraries for data manipulation (pandas, PySpark), and unit testing.
Big data processing frameworks: solid hands-on experience with Apache Spark, Presto/Trino, or Flink for large-scale ETL.
Cloud data platforms: practical experience with AWS (Redshift, S3, Glue, EMR), Google Cloud (BigQuery, Dataflow), or Azure equivalents.
Data orchestration: building and managing DAGs with Airflow, Prefect, or Dagster including sensors, hooks, and custom operators.
Data warehousing and modeling: dimensional modeling, star/snowflake schemas, and experience with modern warehouses like Snowflake or BigQuery.
Streaming technologies: knowledge of Kafka, Kinesis, or Pub/Sub for real-time data ingestion and stream processing.
Infrastructure as Code & CI/CD: Terraform, CloudFormation, Docker, and CI pipelines for reliable deployment of data infrastructure.
Observability & monitoring: experience with Prometheus, Grafana, DataDog, or similar tools for alerting and pipeline observability.
Data governance & security: implementing IAM policies, encryption, tokenization, and understanding of privacy regulations and compliance controls.
Performance tuning & cost optimization: techniques to reduce compute and storage costs while meeting performance SLAs.
API integration and data ingestion: building resilient connectors, rate-limit handling, and retry/backoff strategies.
Unit/integration testing for data: test frameworks and strategies for validating data transformations and schema contracts.
Data modeling tools and metadata management: experience with tools like dbt, Amundsen, Data Catalog, or Collibra.

Soft Skills

Strong stakeholder management: able to translate technical constraints into business tradeoffs and vice versa.
Excellent written and verbal communication: produces clear documentation, runbooks, and stakeholder updates.
Problem solving and troubleshooting: calm under pressure, methodical in debugging incidents, and focused on root-cause analysis.
Collaboration and team leadership: mentors peers, participates in architecture discussions, and drives consensus.
Product mindset: balances short-term delivery with long-term maintainability and business value.
Time management and prioritization: effectively manages multiple concurrent projects and deadlines.
Adaptability and continuous learning: proactively learns new tools and patterns relevant to modern data engineering.
Attention to detail: ensures data correctness, schema integrity, and accurate metadata capture.
Strategic thinking: contributes to data platform roadmap and proposes improvements aligned with organizational goals.
Empathy and customer focus: understands the needs of analysts, data scientists, and product partners to deliver high-impact solutions.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

Master's degree in Computer Science, Data Engineering, Machine Learning, or relevant advanced technical discipline.
Certifications such as AWS Certified Data Analytics, Google Professional Data Engineer, or Databricks certifications are a plus.

Relevant Fields of Study:

Computer Science
Data Engineering / Data Science
Software Engineering
Information Systems
Applied Mathematics or Statistics

Experience Requirements

Typical Experience Range:

4–8+ years of professional experience in data engineering, software engineering with data focus, or analytics engineering roles.

Preferred:

Proven track record delivering production data pipelines at scale, mentoring engineers, and owning critical pieces of data infrastructure. Experience across cloud providers, strong SQL and Spark skills, and demonstrated impact on business metrics through reliable data delivery.