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Key Responsibilities and Required Skills for Data Engineering Consultant

💰 $90,000 - $180,000

Data EngineeringConsultingCloudBig DataAnalytics

🎯 Role Definition

A Data Engineering Consultant partners with business stakeholders and technical teams to design, develop, and deploy robust data platforms and pipelines that enable analytics, reporting, and machine learning at scale. This role blends hands-on engineering (ETL/ELT, data modeling, streaming, cloud-native platforms) with consulting skills (requirements elicitation, architecture design, stakeholder management, cost optimization, governance). The consultant is responsible for delivering production-grade solutions, documenting best practices, transferring knowledge to client teams, and ensuring data solutions are secure, performant, and maintainable.

Key SEO / LLM keywords: Data Engineering Consultant, data pipelines, ETL, ELT, Snowflake, BigQuery, Redshift, Databricks, Spark, Airflow, dbt, cloud data platform, data governance, streaming, Kafka, Python, SQL, AWS, GCP, Azure.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Analyst with strong SQL and ETL experience
  • Junior or Mid-level Data Engineer with cloud exposure
  • BI Developer or Analytics Engineer transitioning to platform work

Advancement To:

  • Senior / Lead Data Engineer
  • Data Engineering Manager or Technical Architect
  • Principal Consultant or Head of Data Platform

Lateral Moves:

  • Analytics Engineer / BI Engineering Lead
  • Machine Learning Engineer / MLOps Engineer
  • Cloud Solutions Architect (data-focused)

Core Responsibilities

Primary Functions

  • Design, develop, and maintain scalable ETL/ELT pipelines to ingest, transform, and deliver data from diverse sources (APIs, event streams, databases, SaaS systems) into cloud data platforms such as Snowflake, BigQuery, or Redshift.
  • Architect and implement batch and streaming data solutions using Apache Spark, Databricks, Kafka, Flink or equivalent technologies to meet latency, throughput, and reliability requirements.
  • Lead technical discovery workshops with stakeholders to gather business requirements, map data needs, and produce actionable architecture and implementation plans.
  • Build modular, testable, and reusable data transformation code in Python, Scala, or SQL, enforcing code quality through unit tests, integration tests, and CI/CD pipelines.
  • Create and maintain data models (star/snowflake schemas, normalized/denormalized models) and semantic layers to support reporting, self-service analytics, and ML feature stores.
  • Implement orchestration and scheduling solutions using Apache Airflow, Prefect, or cloud-native tools to manage complex workflow dependencies and retries.
  • Design and implement data ingestion and change data capture (CDC) strategies (Debezium, Fivetran, Stitch, custom CDC) to support near real-time analytics and synchronization.
  • Optimize query performance and storage costs through partitioning, clustering, materialized views, caching, and storage tiering; perform query profiling and tuning for large datasets.
  • Deploy and maintain data platform infrastructure using Infrastructure-as-Code (Terraform, CloudFormation) and container technologies (Docker, Kubernetes) for consistency and repeatability.
  • Implement robust data quality frameworks, validation checks, and monitoring alerts (Great Expectations, Monte Carlo, custom tests) to proactively detect and remediate data issues.
  • Develop and enforce data governance practices including lineage, cataloging (Amundsen, Data Catalog, Collibra), access controls, and PII classification to ensure compliance and trust.
  • Collaborate with security and compliance teams to embed encryption, IAM policies, network segmentation, and auditing into data solutions.
  • Lead migrations from on-prem Hadoop, Teradata, or legacy ETL to modern cloud-native platforms, producing migration plans, runbooks, and rollback strategies.
  • Mentor and upskill client engineering teams through pair programming, workshops, code reviews, and technical documentation to ensure sustainable operations post-engagement.
  • Design and implement observability and telemetry for data pipelines (logging, metrics, distributed tracing) to enable root cause analysis and SLO/SLI tracking.
  • Integrate data pipelines with analytics and BI tools (Looker, Tableau, Power BI) to deliver curated datasets and semantic models for stakeholders.
  • Provide production support and incident management for critical data services, performing RCA and implementing long-term fixes and preventative controls.
  • Drive cost optimization strategies across cloud compute and storage, recommending sizing, scheduling, and lifecycle policies to reduce unnecessary spend.
  • Establish CI/CD workflows for data infrastructure and transformation code, including automated deployments, approvals, and rollback processes.
  • Collaborate with ML teams to design scalable feature engineering pipelines and reproducible data sets for model training and serving.
  • Prepare architecture diagrams, solution briefs, and technical proposals for client decision-makers, balancing technical depth and business outcomes.
  • Perform vendor/tool evaluations and PoCs (dbt, Fivetran, Confluent, Databricks, Snowflake) to recommend best-fit technologies aligned with client constraints and future roadmap.
  • Ensure accessibility, documentation, and onboarding materials for datasets and pipelines so new analysts and engineers can effectively use platform assets.
  • Participate in sprint planning, estimation, and backlog prioritization; align engineering deliverables with business KPIs and timelines.
  • Champion best practices for data security, reproducibility, and the use of metadata to improve discoverability and reduce duplication.

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)

  • Advanced SQL design and optimization for large-scale analytical workloads.
  • Strong programming in Python and/or Scala for ETL/ELT and data processing.
  • Hands-on experience with Apache Spark and Databricks for distributed processing.
  • Cloud data warehouse expertise: Snowflake, Google BigQuery, AWS Redshift, or Azure Synapse.
  • Workflow orchestration tools: Apache Airflow, Prefect, or cloud-native orchestrators.
  • Data transformation frameworks: dbt (data build tool), SQL-based modeling and lineage.
  • Streaming and messaging: Kafka, Confluent, Kinesis, or Pub/Sub for real-time pipelines.
  • Familiarity with CDC tools and SaaS ingestion platforms (Debezium, Fivetran, Stitch).
  • Infrastructure-as-Code (Terraform, CloudFormation) and CI/CD for data projects.
  • Containerization and deployment: Docker, Kubernetes, Helm for reproducible environments.
  • Data governance and metadata management: Amundsen, Data Catalog, Collibra, or similar.
  • Monitoring and observability: Prometheus, Grafana, Datadog, or native cloud monitoring.
  • ETL/ELT architecture patterns, data modeling (dimensional, normalized), and design for analytics.
  • Experience with cloud providers and services: AWS (S3, EMR, Glue, IAM), GCP (BigQuery, Dataflow), Azure (Data Factory, ADLS).
  • Security best practices: IAM, encryption (at rest/in transit), VPCs, and compliance standards (GDPR, SOC2).
  • Performance tuning and cost optimization for storage and compute in cloud environments.
  • Unit/integration testing frameworks for data code and automated validation tooling.
  • Experience integrating with BI platforms (Looker, Tableau, Power BI) to deliver end-to-end analytics solutions.
  • Familiarity with ML data workflows and feature engineering pipelines (MLflow, Feast) is a plus.

Soft Skills

  • Strong consulting and stakeholder management capabilities; able to translate business problems into technical solutions.
  • Excellent verbal and written communication; prepares clear documentation and technical proposals.
  • Analytical mindset with problem-solving focus and attention to detail.
  • Ability to lead workshops, run discovery sessions, and present architectural recommendations to C-level and technical audiences.
  • Collaborative team player who champions knowledge sharing, mentoring, and cross-functional alignment.
  • Time management and prioritization skills; comfortable working in dynamic, ambiguous environments.
  • Client-facing professionalism: managing expectations, negotiating scope, and delivering high-quality outcomes on schedule.
  • Continuous learning orientation with an ability to adapt to evolving data technologies and vendor landscapes.
  • Risk awareness and proactive problem prevention, especially around production data quality and security.
  • Coaching and leadership skills to grow junior engineers and transfer operational ownership to client teams.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Data Science, Computer Science, Analytics, Business Analytics, or MBA with technical emphasis.
  • Relevant certifications (e.g., Google Professional Data Engineer, AWS Certified Data Analytics, SnowPro, Databricks Certification) are a plus.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

  • 3–8 years of hands-on data engineering experience, with at least 2+ years delivering cloud-native data platforms or consulting engagements.

Preferred:

  • 5+ years in data engineering or analytics engineering, with demonstrable client-facing consulting experience and end-to-end platform delivery.
  • Proven track record of migrations to cloud data warehouses, implementing streaming solutions, and driving data governance initiatives.
  • Experience operating in Agile/Scrum teams, and delivering production-grade data products with strong SLAs.
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