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Key Responsibilities and Required Skills for Data Integration Lead

💰 $ - $

DataEngineeringIntegrationLeadership

🎯 Role Definition

This role requires an experienced Data Integration Lead to own the design, implementation, and operational excellence of enterprise data integration and ingestion solutions. The Data Integration Lead will drive the technical strategy for ETL/ELT pipelines, batch and streaming data flows, API and application integrations, and cloud data platform migrations. This role requires a blend of hands-on engineering, architecture, and people leadership to deliver scalable, secure, and low-latency data movement that powers analytics, reporting, and operational systems.

Key keywords: Data Integration Lead, ETL/ELT, data pipelines, data integration architecture, cloud data platforms, Snowflake, Redshift, BigQuery, Kafka, API integration, real-time streaming, data governance, data quality.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Engineer with ETL/ELT and orchestration experience.
  • Integration Engineer / ETL Developer with strong SQL and cloud skills.
  • Analytics Engineer or Data Platform Engineer transitioning to leadership.

Advancement To:

  • Head of Data Engineering / Head of Data Integration
  • Director of Data Platforms or Director of Data Engineering
  • VP of Data & Analytics or Chief Data Officer (CDO)

Lateral Moves:

  • Solutions Architect (Data/Integration)
  • Cloud Platform Architect (AWS/Azure/GCP)
  • Product Manager, Data Platform

Core Responsibilities

Primary Functions

  • Lead the end-to-end design and implementation of enterprise ETL/ELT pipelines and data integration patterns across batch and streaming workflows to deliver timely, accurate, and performant datasets for analytics, reporting, and machine learning.
  • Define and own the data integration architecture and roadmap, including selection and governance of integration tools (e.g., Informatica, Talend, Matillion, Fivetran, Stitch, MuleSoft) and cloud-native services to align with the company's data strategy.
  • Architect and build scalable data ingestion solutions for cloud data warehouses and lakes (Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse) ensuring optimal cost-performance tradeoffs and efficient data modeling.
  • Lead the migration of on-premises ETL workloads to cloud-native ELT patterns, including refactoring legacy pipelines, benchmarking, and performance tuning to leverage modern data warehousing features.
  • Design and implement robust CDC (Change Data Capture) and real-time streaming ingestion solutions using Kafka, Kinesis, Debezium, or equivalent technologies to enable low-latency data synchronization across systems.
  • Establish and enforce best practices for data integration development: modular pipeline patterns, parameterization, testing, version control, and reusable components to accelerate delivery and reduce technical debt.
  • Implement orchestration and scheduling solutions (Apache Airflow, Control-M, AWS Step Functions) with retry logic, SLA monitoring, and alerting to ensure reliable pipeline execution and timely incident response.
  • Collaborate with data governance, security, and compliance teams to implement data lineage, cataloging, masking, encryption, and access controls that meet regulatory and internal policy requirements.
  • Develop and maintain CI/CD pipelines, infrastructure-as-code (Terraform, CloudFormation), and containerized deployments (Docker, Kubernetes) for reproducible, auditable data integration deployments.
  • Drive data quality frameworks and automated validation checks across ingestion and transformation layers to detect schema drift, data anomalies, and completeness gaps before downstream consumption.
  • Lead capacity planning, performance testing, and cost optimization initiatives for integration workloads on cloud platforms and managed services to ensure predictable SLAs and budget control.
  • Mentor, hire, and grow a cross-functional team of data engineers and integration specialists, setting technical direction, conducting code reviews, and fostering a culture of continuous improvement.
  • Partner closely with analytics, data science, and business stakeholders to translate business requirements into integration designs, prioritizing use cases based on impact, feasibility, and strategic value.
  • Manage third-party vendor relationships and integration projects (SaaS connectors, API vendors), including scoping, security reviews, SLA negotiations, and contract governance to streamline integrations.
  • Create and maintain comprehensive technical documentation, runbooks, and operational playbooks for data integration pipelines, including recovery procedures and post-incident analysis.
  • Implement monitoring, observability, and alerting dashboards (Prometheus, Grafana, CloudWatch, Stackdriver) to provide actionable insights into pipeline health, latency, throughput, and error rates.
  • Establish data modeling and semantic layer practices with warehouse teams to ensure integrated datasets are curated, well-documented, and optimized for downstream BI and analytics consumption.
  • Lead proof-of-concept initiatives for emerging integration technologies (serverless ETL, Data Mesh patterns, event-driven architectures) and evaluate their applicability to improve agility and scalability.
  • Ensure integrations adhere to security and privacy standards (PII handling, GDPR, HIPAA when applicable), including secure credential management, least-privilege access, and audit logging.
  • Coordinate cross-functional release planning and change management for integration deployments to minimize downstream disruption and ensure stakeholder alignment.
  • Troubleshoot complex, cross-system integration issues involving source systems, APIs, network configurations, and downstream consumers, leading root-cause analyses and driving long-term remediation.
  • Define KPIs and metrics to measure integration effectiveness, data freshness, accuracy, and system reliability, and report outcomes to senior leadership for continuous improvement.

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)

  • Proven expertise in ETL/ELT design and development with tools like Informatica, Talend, Matillion, SSIS, Fivetran, or custom Spark/Python pipelines.
  • Strong SQL skills for complex query optimization, window functions, and performance tuning on large datasets across Snowflake, Redshift, BigQuery, or Azure Synapse.
  • Hands-on experience with cloud platforms and services (AWS, Azure, GCP) for data ingestion, storage, compute, and serverless integration patterns.
  • Proficiency in data processing frameworks and languages such as Apache Spark, PySpark, Scala, Python, or Java for high-volume transformations and streaming analytics.
  • Experience designing and operating streaming architectures using Apache Kafka, AWS Kinesis, Debezium, or equivalent event-driven messaging systems.
  • Familiarity with API integration, middleware, and ESB technologies (MuleSoft, Apigee) and designing robust RESTful or GraphQL ingestion flows.
  • Competence with orchestration and workflow schedulers like Apache Airflow, Prefect, Luigi, or enterprise schedulers (Control-M) for dependency management and SLA tracking.
  • Knowledge of data modeling, dimensional modeling, star/snowflake schemas, and best practices for analytical schema design.
  • Strong background in data governance, metadata management, data lineage, MDM concepts, and implementing solutions with tools such as Collibra, Informatica EDC, or Alation.
  • Experience with CI/CD, infrastructure-as-code (Terraform, CloudFormation), containerization (Docker), and orchestration platforms (Kubernetes) to automate deployments and environment provisioning.
  • Monitoring and observability experience using tools such as Prometheus, Grafana, Datadog, CloudWatch, or Stackdriver to ensure operational reliability.
  • Security and compliance knowledge including encryption, IAM, token-based authentication, secrets management, and handling regulated data (PII, HIPAA, GDPR).

Soft Skills

  • Strong leadership and team development skills with experience mentoring engineers and fostering a collaborative, high-performance culture.
  • Excellent stakeholder management and communication skills to align technical solutions with business priorities and present technical concepts to non-technical audiences.
  • Strategic thinking with the ability to develop a multi-quarter data integration roadmap aligned to company objectives.
  • Strong analytical and problem-solving skills to diagnose complex integration failures and drive systemic fixes.
  • Project and program management skills with experience delivering cross-functional initiatives on schedule and within budget.
  • Customer-focused mindset and service orientation to prioritize integrations that deliver measurable business value.
  • Adaptability and continuous learning to evaluate new integration patterns, tools, and cloud innovations.
  • Conflict resolution and negotiation skills to manage vendor relationships, scope changes, and stakeholder expectations.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Software Engineering, Data Engineering, Electrical Engineering, or related technical field.

Preferred Education:

  • Master's degree in Computer Science, Data Science, Information Systems, or MBA with technical emphasis is a plus.

Relevant Fields of Study:

  • Computer Science
  • Data Engineering / Data Science
  • Information Systems / Management Information Systems
  • Software Engineering
  • Electrical or Computer Engineering

Experience Requirements

Typical Experience Range:

  • 7–12+ years of professional experience in data engineering, ETL/ELT development, or systems integration, with at least 3–5 years in a leadership or technical lead capacity.

Preferred:

  • Hands-on experience leading enterprise-scale data integration programs, migrating ETL to cloud ELT patterns, and managing cross-functional teams; prior experience with Snowflake, Redshift, BigQuery, Kafka, Airflow, and industry ETL tools strongly preferred.
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