Data Analytics/Integration Engineer (6-9 yrs)

A Data Analytics Integration Engineer is responsible for designing, developing, and managing the integration of data from various sources into analytics platforms, ensuring high-quality data pipelines, and enabling insightful reporting and analysis. The role bridges the gap between data engineering, integration, and analytics teams, supporting decision-making through reliable data processes.

Key Responsibilities :

Data Integration :

- Design and implement data integration pipelines to collect, transform, and load data from diverse sources into analytics platforms.

- Ensure seamless integration of data from structured, semi-structured, and unstructured data sources (e.g., databases, APIs, flat files).

ETL/ELT Development :

- Develop ETL/ELT processes to cleanse, transform, and aggregate data for reporting and analytics purposes.

- Optimize data workflows for performance and scalability.

Collaboration and Requirements Gathering :

- Work closely with business analysts, data scientists, and other stakeholders to understand data requirements.

- Translate business needs into technical specifications for data integration solutions.

Data Quality and Governance :

- Implement and enforce data quality checks to ensure accuracy, completeness, and consistency of integrated data.

- Adhere to data governance standards and compliance regulations, including data privacy and security.

Platform and Tool Management :

- Maintain and configure data integration platforms and tools (e.g., Talend, Informatica, Apache Nifi, Azure Data Factory, or similar).

- Monitor and troubleshoot integration processes to address failures and ensure uptime.

Analytics Enablement :

- Collaborate with analytics teams to create datasets and models optimized for visualization and decision-making.

- Support reporting and dashboarding tools like Power BI, Tableau, or Looker by integrating relevant datasets.

Documentation :

- Create and maintain comprehensive documentation for data workflows, integrations, and processes.

- Document best practices and ensure knowledge sharing within the team.

Innovation and Continuous Improvement :

- Stay updated with emerging data integration tools and techniques.

- Recommend and implement improvements to data integration architectures.

Required Skills and Qualifications :

Technical Expertise :

- Proficiency in ETL/ELT tools like Informatica, Talend, Apache Nifi, or Azure Data Factory.

- Strong SQL skills for data extraction, transformation, and optimization.

- Experience with data warehouse solutions like Snowflake, Redshift, or BigQuery.

- Familiarity with scripting languages (e.g., Python, Java, or Scala) for data transformation tasks.

Data Knowledge :

- Solid understanding of data modeling, data warehousing, and database architecture.

- Familiarity with APIs and methods for integrating external data sources.

Analytics and BI Tools :

- Experience with visualization and analytics tools like Tableau, Power BI, Looker, or Qlik.

- Knowledge of creating datasets for business reporting purposes.

Soft Skills :

- Strong problem-solving and analytical skills.

- Excellent communication and interpersonal abilities to work with cross-functional teams.

- Attention to detail and commitment to maintaining high-quality data standards.

Preferred Qualifications :

- Bachelor's degree in Computer Science, Data Science, Information Systems, or a related field.

- 3+ years of experience in data integration or related roles.

- Knowledge of cloud platforms like AWS, Azure, or GCP for data integration and analytics.

- Experience with big data frameworks such as Spark, Hadoop, or Kafka.

- Certifications in relevant tools/platforms (e.g., Informatica, Azure Data Engineer).

A Data Analytics Integration Engineer ensures that data flows smoothly across systems, supports analytical initiatives, and empowers decision-makers with accurate and timely insights. This role is vital for organizations aiming to leverage their data effectively.