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Key Responsibilities and Required Skills for Data Integration Engineer

💰 $90,000 - $160,000

Data EngineeringIntegrationETL/ELTCloud Data Platforms

🎯 Role Definition

This role requires a Data Integration Engineer who designs, builds, and operates reliable, scalable, and secure data integration solutions to support analytics, BI, machine learning, and operational use cases. The ideal candidate has deep experience building ETL/ELT pipelines, cloud data platform expertise (Snowflake/Redshift/BigQuery), orchestration (Airflow/Prefect), streaming (Kafka), and strong SQL and Python engineering skills. This role partners with data scientists, analysts, product teams, and platform engineers to deliver high-quality, governed, and well-documented data products.

Keywords: Data Integration Engineer, ETL, ELT, data pipelines, data ingestion, Snowflake, BigQuery, Redshift, Airflow, dbt, Kafka, Python, SQL, cloud data platforms, data quality, data governance.

📈 Career Progression

Typical Career Path

Entry Point From:

  • ETL Developer
  • Data Engineer
  • BI Developer / Analytics Engineer

Advancement To:

  • Senior Data Integration Engineer
  • Data Architect / Cloud Data Architect
  • Engineering Manager / Head of Data Platform

Lateral Moves:

  • Analytics Engineer (dbt-focused)
  • Data Quality / Data Governance Specialist
  • Machine Learning Infrastructure Engineer

Core Responsibilities

Primary Functions

  • Design, develop, and maintain scalable ETL/ELT pipelines using SQL, Python, and modern data stack tools (Airflow, dbt, Azure Data Factory, AWS Glue) to reliably ingest, transform, and deliver data from multiple on-prem and cloud sources to Snowflake, BigQuery, Redshift, or equivalent data warehouses.
  • Build and operate data orchestration and workflow systems (Apache Airflow, Prefect, Dagster), including DAG design, task retries, SLA definitions, alerting, and failure remediation procedures.
  • Implement change data capture (CDC) and incremental load strategies using Debezium, Kafka Connect, Striim, Fivetran, or custom CDC patterns to enable near real-time ingestion and to minimize load times and cost.
  • Design and enforce data modeling best practices (star schema, dimensional modeling, normalized models) to optimize query performance, enable analytics, and support BI semantics.
  • Collaborate with data consumers (analytics, product, operations, ML teams) to translate business requirements into robust data integration specifications, source-to-target mappings, and validation rules.
  • Create and maintain comprehensive data lineage, metadata, and documentation using tools like Amundsen, DataHub, Collibra, or internal metadata solutions to ensure discoverability and governance.
  • Build automated testing frameworks for integration pipelines (unit tests, integration tests, data acceptance tests, schema drift detection) and integrate tests into CI/CD pipelines.
  • Optimize pipeline performance and manage costs by tuning SQL, partitioning strategies, clustering, resource allocation, and by using best practices for cloud data warehouse optimization.
  • Lead or contribute to data migration projects, moving legacy ETL processes to cloud-native ELT patterns, including rearchitecting batch jobs for streaming where appropriate.
  • Implement robust data quality checks and monitoring (Great Expectations, custom checks) with alerting and auto-remediation strategies to maintain SLA commitments.
  • Integrate and manage streaming ingestion pipelines with Apache Kafka, Kinesis, or Pub/Sub for event-driven architectures and ensure exactly-once/at-least-once semantics as required.
  • Establish and enforce security, access controls, and data encryption standards for pipeline development and runtime, ensuring compliance with GDPR, CCPA, HIPAA, or internal security policies.
  • Design and deploy APIs and ingestion endpoints (REST, GraphQL, SFTP, message queues) for third-party and internal data producers and consumers, including payload validation and schema evolution support.
  • Implement schema management and evolution strategies (Avro, Protobuf, JSON Schema) and manage backward/forward compatibility for producers and consumers.
  • Participate in capacity planning, forecasting, and scaling strategies for data platform resources, including recommending hardware or cloud configurations to meet throughput requirements.
  • Instrument pipelines and infrastructure with monitoring and observability (Prometheus, Grafana, CloudWatch, Stackdriver) to track latency, throughput, error rates, and resource utilization.
  • Drive automation and standardization across the integration lifecycle, including templated pipeline components, reusable connectors, and developer onboarding docs to reduce time-to-value.
  • Troubleshoot complex production incidents, lead root cause analysis (RCA), document findings, and implement preventive measures to reduce recurrence.
  • Mentor junior engineers and share best practices across cross-functional teams to improve code quality, design patterns, and operational readiness.
  • Collaborate with platform and DevOps teams to define CI/CD for data pipelines, using Git, GitOps, Terraform, and containerization (Docker) to ensure repeatable deployments.
  • Evaluate, propose, and pilot new integration technologies and managed services (Fivetran, Stitch, Matillion, Meltano, Airbyte) to accelerate onboarding and lower operational overhead.
  • Ensure consistent tagging, cataloging, and compliance of sensitive data sets, coordinating with data governance to implement masking, tokenization, and role-based access.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to accelerate stakeholder decision-making.
  • Contribute to the organization's data strategy and integration roadmap, prioritizing work aligned with business impact and technical feasibility.
  • Collaborate with business units to translate data needs into engineering requirements and acceptance criteria.
  • Participate in sprint planning, agile ceremonies, and cross-team design reviews within the data engineering organization.
  • Provide cost optimization recommendations for storage and compute in cloud data platforms.
  • Run periodic audits on data pipelines for performance, security, and compliance, and prepare executive summaries of findings and remediation plans.
  • Assist in vendor selection and contract evaluation for integration and pipeline tooling, including proof-of-concept leadership.
  • Act as a liaison between data governance, security, and engineering to implement access controls and data retention policies.
  • Deliver training sessions and create runbooks and troubleshooting guides to upskill internal users and reduce support burden.
  • Contribute to open-source or internal SDKs/connectors to expand the catalog of available adapters for common enterprise systems (ERP, CRM, marketing platforms).

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL expertise for complex transformations, window functions, CTEs, query tuning, and performance debugging.
  • Strong programming skills in Python (preferred), Scala, or Java for pipeline development, ETL logic, and tooling integration.
  • Experience building ELT workflows with dbt (data build tool) or similar transformation frameworks.
  • Hands-on experience with cloud data warehouses and data lakes: Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse, or Delta Lake.
  • Orchestration and workflow management with Apache Airflow, Prefect, Dagster, or cloud-native schedulers.
  • Familiarity with streaming platforms and event-driven architectures: Apache Kafka, Kinesis, Pub/Sub, and related tooling (Kafka Connect, Debezium).
  • Experience with managed integration tools and connectors: Fivetran, Stitch, Matillion, Airbyte, or proprietary ETL tools such as Informatica/Talend.
  • Knowledge of data modeling (Kimball, Inmon) and schema design for analytics and BI consumption.
  • Proficient with API integration patterns, RESTful services, message queues, JSON, XML and OAuth/JWT authentication patterns.
  • Experience with CI/CD pipelines for data engineering using Git, Jenkins, GitHub Actions, or GitLab CI; familiarity with infrastructure-as-code (Terraform, CloudFormation).
  • Understanding of data governance, metadata management, privacy regulations (GDPR, CCPA), and techniques for masking/tokenization of PII.
  • Familiarity with big data processing frameworks: Apache Spark (PySpark), EMR, Dataproc.
  • Monitoring and observability tools experience: Prometheus/Grafana, Cloud monitoring, Sentry, Datadog.
  • Containerization and orchestration basics: Docker; exposure to Kubernetes is a plus.
  • Familiarity with cost optimization techniques for cloud storage, compute, and query costs.

Soft Skills

  • Strong stakeholder management and the ability to translate business questions into technical solutions and clear deliverables.
  • Excellent written and verbal communication skills for documentation, runbooks, and cross-functional collaboration.
  • Analytical problem-solving mindset with attention to detail and a passion for data quality and correctness.
  • Ability to prioritize and manage multiple concurrent projects with changing requirements and tight deadlines.
  • Collaborative team player who mentors others, gives constructive feedback, and contributes to a healthy engineering culture.
  • Proactive ownership and accountability for production systems and SLAs.
  • Comfort working in Agile environments and participating in iterative delivery and continuous improvement.
  • Ability to present technical concepts to non-technical audiences and influence product decisions through data.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Computer Science, Data Science, Engineering, or MBA with a technical background.
  • Certifications such as SnowPro, Google Professional Data Engineer, AWS Certified Data Analytics, dbt Labs Certification, or Confluent Kafka certifications.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 3–7 years building ETL/ELT or data integration solutions; 5+ years preferred for senior roles.

Preferred:

  • 3+ years designing and operating cloud-based data pipelines (Snowflake, BigQuery, Redshift).
  • Proven track record integrating data from SaaS platforms (Salesforce, HubSpot), databases (Postgres, MySQL, Oracle), and event streams.
  • Demonstrated experience with orchestration (Airflow) and data transformation frameworks (dbt, Spark).
  • Experience in building monitoring, testing, and CI/CD for data pipelines, with solid production troubleshooting experience.
  • Prior exposure to data governance, security, and compliance frameworks in enterprise environments.
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