Key Responsibilities and Required Skills for Data Integration Specialist

🎯 Role Definition

The Data Integration Specialist is a hands-on data engineering professional responsible for designing, building, and maintaining robust, secure, and scalable data integration solutions. This role focuses on end-to-end integration of disparate data sources — databases, SaaS applications, streaming platforms, and APIs — into centralized data warehouses and lakes for analytics, reporting, and operational use. The ideal candidate blends strong ETL/ELT technical expertise with data modeling, data quality, and stakeholder-facing communication skills to deliver timely, reliable data flows that support business objectives.

📈 Career Progression

Typical Career Path

Entry Point From:

ETL Developer / Junior Data Engineer
Business Intelligence (BI) Developer
Systems Integration Analyst

Advancement To:

Senior Data Engineer / Senior Integration Engineer
Data Architect / Integration Architect
Manager of Data Engineering / Head of Data Integration

Lateral Moves:

Analytics Engineer (dbt-focused)
Data Platform Engineer
DevOps / MLOps Engineer

Core Responsibilities

Primary Functions

Design, develop, and maintain enterprise-grade ETL/ELT pipelines that extract data from relational databases, NoSQL stores, SaaS applications (Salesforce, HubSpot, Workday), and flat-file feeds into cloud data platforms such as Snowflake, Redshift, BigQuery, Azure Synapse, or datalake architectures.
Build robust API-based integrations using RESTful services, OAuth, and JSON/XML transformations to reliably ingest third-party and internal application data with retry, backoff, and idempotency patterns.
Implement change data capture (CDC) solutions and real-time streaming integrations using Kafka, Debezium, AWS Kinesis, or Google Pub/Sub to support low-latency analytics and operational reporting.
Author scalable transformation logic in SQL, Python, or Scala to normalize, deduplicate, and enrich incoming datasets, applying best practices for performance, maintainability, and observability.
Automate data ingestion and orchestration workflows with orchestration tools such as Apache Airflow, Azure Data Factory, AWS Glue, or similar scheduling and dependency management systems.
Develop and enforce data mapping, data lineage, and metadata capture so downstream consumers understand origin, transformation rules, and ownership for every data asset.
Integrate and maintain connectors and ELT tools (Fivetran, Stitch, Matillion, Talend, Informatica, SSIS) ensuring connector health, schema drift handling, and cost-effective operations.
Collaborate with data architects and product owners to design source-to-target mappings, canonical models, and dimensional models that enable reliable reporting and BI consumption.
Implement robust data validation, data profiling, and data quality checks using frameworks and tools (Great Expectations, custom validators) with alerting and remediation workflows.
Monitor pipeline performance, SLA adherence, and data freshness metrics; proactively identify bottlenecks and perform capacity planning and performance tuning.
Design and maintain CI/CD pipelines for integration code and infrastructure as code (Terraform, CloudFormation, Azure ARM) to support repeatable, auditable deployments across environments.
Create, review, and maintain comprehensive technical documentation — runbooks, integration specifications, transformation logic, and API contracts — to support operational handoffs and audits.
Enforce security controls for data-in-transit and data-at-rest, including encryption, key management, role-based access controls, and adherence to GDPR, HIPAA, SOC2, or other regulatory requirements as applicable.
Implement error handling, retry strategies, dead-letter queues, and incident escalation procedures to ensure resilience and minimize data loss.
Lead root cause analysis and post-incident reviews for data failures, documenting fixes, preventive measures, and follow-up tasks to improve system reliability.
Conduct source system discovery and data profiling to assess data quality, volume characteristics, update frequency, and integration complexity prior to solution design.
Translate business requirements and analytics use-cases into technical integration specifications and realistic delivery timelines while managing stakeholder expectations.
Collaborate with BI teams, data scientists, and analysts to deliver curated datasets, semantic layers, and reliable feeds for dashboards, ML models, and reporting platforms.
Perform schema evolution management: implement schema versioning strategies, backward-compatible transformations, and automated schema migration processes to minimize downstream impacts.
Evaluate, pilot, and recommend third-party integration tools, middleware, or managed services that reduce technical debt, lower operational overhead, and accelerate time-to-value.
Manage data pipeline cost optimization by tuning resource usage, scheduling, and storage lifecycle policies while balancing performance and business SLAs.
Mentor and coach junior integration engineers, perform code reviews, and establish team standards for testing, documentation, and observability instrumentation.
Maintain vendor relationships and coordinate upgrades, security patches, and support escalations for hosted integration platforms and connectors.
Ensure traceability and auditability of data flows by implementing logging, lineage visualization, and analytics for usage and governance reporting.

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.
Assist in building data catalogs and tagging schemes to improve data discoverability and self-service analytics.
Provide onboarding and training sessions for analytics consumers on data availability, SLA expectations, and usage patterns.

Required Skills & Competencies

Hard Skills (Technical)

SQL proficiency: advanced query tuning, window functions, CTEs, and performance optimization for large datasets.
ETL/ELT tools: hands-on experience with tools such as Informatica, Talend, SSIS, Fivetran, Stitch, Matillion, or AWS Glue.
Cloud data platforms: working knowledge of Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse.
Programming & scripting: Python (pandas, PySpark), Scala, or Java for transformations, data processing, and automation.
Orchestration & workflow: experience with Apache Airflow, Azure Data Factory, Luigi, or managed orchestration services.
Streaming & CDC technologies: Kafka, Debezium, Kinesis, Pub/Sub, or similar event-driven ingestion frameworks.
APIs & integrations: RESTful API design, OAuth, JSON/XML parsing, and API gateway experience.
Data modeling: dimensional modeling, star/snowflake schemas, and normalization for data warehousing.
Data quality & profiling: tools/practices such as Great Expectations, Deequ, custom validators, or data profiling suites.
DevOps & CI/CD: Git, Jenkins/GitHub Actions/GitLab CI, Docker, and infrastructure as code (Terraform, CloudFormation).
Monitoring & observability: Prometheus, Grafana, Datadog, Sentry, or cloud-native monitoring for pipeline health and SLAs.
Security & compliance: encryption, RBAC, IAM, and familiarity with GDPR, HIPAA, SOC2, or industry-specific regulations.
Schema & metadata management: experience with data catalogs (Collibra, Alation), lineage tools, and schema registry concepts.
Performance tuning & cost optimization: query optimization, partitioning, clustering, and cloud cost management strategies.

Soft Skills

Strong stakeholder communication: translate technical constraints into business-focused updates and commitments.
Analytical problem solving: diagnose complex pipeline failures and propose pragmatic mitigation and long-term fixes.
Attention to detail: meticulous validation of mapping logic, edge cases, and transformation outputs.
Collaboration: experience working cross-functionally with product managers, analysts, data scientists, and operations.
Time management and prioritization: balance rapid operational fixes with long-term architecture improvements.
Ownership and accountability: drive initiatives end-to-end and follow through on incident remediation and documentation.
Mentorship and coaching: teach best practices, perform code reviews, and elevate team skill levels.
Adaptability and continuous learning: keep pace with evolving cloud services, integration patterns, and tooling.
Agile mindset: comfortable working in sprints, breaking down requirements, and iterating with stakeholders.
Customer-oriented approach: focus on delivering reliable, well-documented data products that meet consumer needs.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in Computer Science, Information Systems, Software Engineering, Data Science, or a related technical discipline; or equivalent practical experience.

Preferred Education:

Master’s degree in Computer Science, Data Engineering, Analytics, or MBA with strong technical coursework.
Relevant certifications (AWS Certified Data Analytics, Google Professional Data Engineer, Snowflake SnowPro, Informatica/Talend certifications) are a plus.

Relevant Fields of Study:

Computer Science / Software Engineering
Information Systems / Data Analytics
Mathematics, Statistics, or Applied Sciences
Business Intelligence / Data Management

Experience Requirements

Typical Experience Range:

3–7 years of experience in data integration, ETL/ELT development, or data engineering roles for mid-level positions.
5+ years preferred for senior specialist roles or positions requiring deep platform and architecture experience.

Preferred:

Demonstrated experience integrating SaaS applications and enterprise systems at scale.
Proven track record delivering production-grade data pipelines in cloud environments with SLAs and monitoring.
Experience working in regulated industries (finance, healthcare, telecom) is an advantage.