Data Engineer

We are seeking an experienced Data Engineer to design, implement, and maintain large-scale data pipelines, focusing on ETL using Talend or Informatica, to deliver high-quality analytics-ready data. You'll collaborate with data scientists, BI analysts, and stakeholders to ensure robust data quality, meaningful analytics outputs, and scalable solutions.

Responsibilities :

- Design, develop, and schedule ETL workflows using Talend or Informatica.

- Integrate data from diverse sources (e. g., RDBMS, flat files, SaaS, API endpoints).

- Monitor job performance; optimize for reliability and scale.

- Implement and enforce data quality checks, validation rules, and cleansing routines.

- Establish data profiling and correction processes.

- Collaborate with data governance to ensure compliance and documentation.

- Partner with analytics teams to deliver clean, structured data for dashboards and ML pipelines.

- Pre-process data, define KPIs, and conduct feature engineering.

- Support reporting and visualization needs as required.

- Develop Python scripts or modules for data ingestion, transformation, and orchestration.

- Write complex SQL queries for ETL logic, data validation, aggregation, and analytics.

- Automate workflows, anomaly detection, and data-driven triggers.

- Design seamless API calls for data ingestion (batch or incremental).

- Tune database queries and ETL components for performance.

- Collaborate with infrastructure teams on database design and scaling.

- Maintain detailed technical documentation, including data models and ETL logic flows.

- Participate in agile ceremonies and code reviews.

- Mentor junior engineers and promote best practices.

Requirements :

- ETL Tools : Proven experience with Talend or Informatica in production settings.

- Data Quality Practices : Deep understanding of profiling, validation, cleansing, and governance.

- Strong coding skills in Python for ETL, automation, and transformation.

- Ability to write and optimize advanced SQL queries across relational databases.

- Hands-on experience with analytics pipelines, KPI definitions, and data modeling.

- Excellent verbal and written communication for cross-team collaborations.

- Experience with cloud platforms (AWS, GCP, or Azure) and modern data stack (e. g., Airflow, Snowflake, Big Query).

- Prior exposure to Spark, Scala, or Databricks environments.

- Experience with BI tools like Tableau, Power BI, or Looker.

- Familiarity with microservices, RESTful API integration, and containerization (Docker/Kubernetes)