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Key Responsibilities and Required Skills for Information Engineer

💰 $ - $

Data & AnalyticsEngineeringIT

🎯 Role Definition

The Information Engineer is responsible for architecting, building, and operationalizing robust data infrastructure and information flows that convert raw source data into governed, consumable datasets for analytics, BI, reporting, and machine learning. This role blends data engineering, data modeling, metadata management, ETL design, and cross-functional stakeholder collaboration to ensure data is accurate, discoverable, secure, and performant across cloud platforms (AWS, Azure, GCP) and modern data stacks (Snowflake, BigQuery, Redshift, Databricks).

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Engineer or Data Engineering Intern
  • Business Intelligence (BI) Analyst or ETL Developer
  • Software Engineer with data-focused responsibilities

Advancement To:

  • Senior Information Engineer / Senior Data Engineer
  • Data Architect or Enterprise Data Architect
  • Lead Data Engineer / Head of Data Platforms

Lateral Moves:

  • Analytics Engineer / BI Engineer
  • Data Product Manager
  • Machine Learning Engineer (with added ML experience)

Core Responsibilities

Primary Functions

  • Design, implement, and maintain end-to-end data pipelines (batch and streaming) that reliably ingest, transform, and deliver data from diverse sources into analytic platforms, using technologies such as Apache Airflow, Kafka, Spark, dbt, and native cloud services.
  • Translate business requirements and analytics use cases into technical designs, data models, ETL processes, and data contracts that ensure downstream consumers have timely, accurate, and well-documented datasets.
  • Build scalable, maintainable ETL/ELT workflows using SQL, Python, Scala, or Spark, optimizing for performance, costs, and observability on cloud data warehouses (Snowflake, BigQuery, Redshift) and data lakes.
  • Implement data ingestion frameworks and connectors for relational databases, APIs, message queues, event streams, SaaS sources, and file systems while ensuring schema evolution and error handling are managed gracefully.
  • Own and evolve the enterprise data catalog, metadata management, and lineage tracking to enable data discovery, impact analysis, and compliance with internal policies and external regulations.
  • Establish and enforce data quality frameworks and automated validation checks (unit tests, anomaly detection, reconciliations) to proactively detect, alert, and remediate data issues in CI/CD pipelines.
  • Collaborate with data scientists, analysts, product managers, and application teams to define SLAs, data contracts, and service-level objectives for data delivery and data product reliability.
  • Implement and maintain data governance controls including access controls, role-based permissions, encryption, masking, and PII/PHI handling in alignment with security and privacy standards (GDPR, HIPAA, SOC2).
  • Design and implement robust monitoring, alerting, and observability for data pipelines and data stores (logs, metrics, lineage) using tools such as Prometheus, Grafana, Datadog, or cloud-native monitoring.
  • Optimize storage, compute, and query performance through partitioning, clustering, indexing, caching, and materialized views to reduce latency and cost for analytics workloads.
  • Develop reusable data engineering libraries, templates, and infrastructure-as-code (IaC) modules (Terraform, CloudFormation) to standardize deployments and accelerate onboarding for new data sources.
  • Lead production incident investigations and post-mortems for data outages, perform root-cause analysis, and implement preventative measures to reduce recurrence and improve system resilience.
  • Implement CI/CD practices for data engineering including version control (Git), automated testing, schema migrations, and orchestrated deployments to reduce risk and speed delivery.
  • Partner with security, compliance, and legal teams to design privacy-first data architectures, anonymization strategies, and audit trails for sensitive information access and usage.
  • Create technical documentation, runbooks, API docs, and onboarding guides for data models, pipelines, and ingestion processes to support self-service analytics and knowledge transfer.
  • Prototype and productionize data features for machine learning pipelines, ensuring feature stores, feature engineering jobs, and model inputs are reproducible and monitored in production.
  • Migrate legacy ETL systems to modern data platforms and cloud-native services, including re-architecting workflows for higher scalability and lower operational overhead.
  • Maintain strong source control discipline for SQL and code artifacts, implement code review practices, and mentor junior engineers on best practices for quality and maintainability.
  • Manage schema design and data modeling (dimensional, normalized, and federated architectures) to support canonical business entities and consistent analytics across reporting domains.
  • Evaluate and integrate new data platform tools, frameworks, and managed services to continuously improve developer productivity, data quality, and operational cost-efficiency.
  • Provide hands-on support for production deployments, hotfixes, and data reconciliations, ensuring critical business reports and dashboards remain accurate and available.

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 for data modeling, complex joins, window functions, and performance tuning.
  • Programming proficiency in Python and/or Scala for ETL, data transformation, and automation scripts.
  • Experience building and maintaining data pipelines with orchestration tools (Apache Airflow, Prefect, Luigi).
  • Hands-on experience with streaming technologies (Apache Kafka, Kinesis, Pub/Sub) and stream processing (Spark Streaming, Flink).
  • Working knowledge of modern cloud data warehouses and lakehouses: Snowflake, Google BigQuery, Amazon Redshift, Databricks, Delta Lake.
  • Experience with data transformation frameworks such as dbt and familiarity with ELT patterns.
  • Familiarity with data catalogs, metadata tools, and governance platforms (Collibra, Alation, Amundsen).
  • Strong understanding of data modeling techniques (star schema, normalized models, data vault).
  • Implementing data quality and observability frameworks (Great Expectations, Soda, Deequ).
  • Expertise with RESTful APIs, webhooks, and connector development for data ingestion.
  • Infrastructure-as-code and cloud provisioning skills (Terraform, CloudFormation) and working knowledge of AWS/Azure/GCP services (S3, EMR, Glue, BigQuery, GCS).
  • Experience with containerization and orchestration (Docker, Kubernetes) for deploying data services.
  • Familiarity with CI/CD pipelines, automated testing, and Git-based workflows.
  • Understanding of security principles for data (IAM, encryption at rest/in transit, tokenization, masking).
  • Experience with BI tooling and data consumption (Looker, Tableau, Power BI) and enabling data products for self-service analytics.

Soft Skills

  • Excellent communicator with the ability to translate technical concepts to business stakeholders and vice versa.
  • Strong problem-solving and analytical mindset, able to debug complex production data issues under pressure.
  • Collaborative team player who works cross-functionally with analytics, product, and engineering teams.
  • Proactive ownership and accountability for delivering reliable data products and services.
  • Detail-oriented with strong documentation, process discipline, and test-first mentality.
  • Mentoring and knowledge-sharing orientation to uplift junior engineers and promote best practices.
  • Adaptable to rapidly changing requirements and comfortable working in agile environments.
  • Strong stakeholder management and the ability to prioritize competing business requests.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master’s degree in Data Science, Computer Science, Software Engineering, or Business Analytics; or equivalent professional experience/certifications.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

  • 3–7+ years in data engineering, information engineering, ETL development, or related roles.

Preferred:

  • 5+ years working with cloud data platforms, data warehousing, and production-grade ETL/ELT pipelines.
  • Prior experience in enterprises or high-growth SaaS/FinTech/HealthTech environments with complex data ecosystems.
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