Key Responsibilities :

- Design end-to-end data solutions, including data lakes, data warehouses, ETL/ELT pipelines, APIs, and analytics platforms.

- Architect scalable and low-latency data pipelines using tools like Apache Kafka, Flink, or Spark Streaming to handle high-velocity data streams.

- Design /Orchestrate end-to-end automation using orchestration frameworks such as Apache Airflow to manage complex workflows and dependencies.

- Design intelligent systems that can detect anomalies, trigger alerts, and automatically reroute or restart processes to maintain data integrity and availability.

- Develop scalable data architecture strategies that support advanced analytics, machine learning, and real-time data processing.

- Define and implement data governance, metadata management, and data quality standards.

- Lead architectural reviews and technical design sessions to guide solution development.

- Partner with business and IT teams to translate business needs into data architecture requirements.

- Explore appropriate tools, platforms, and technologies aligned with organizational standards.

- Ensure security, compliance, and regulatory requirements are addressed in all data solutions.

- Evaluate and recommend improvements to existing data architecture and processes.

- Provide mentorship and guidance to data engineers and technical teams.

Technical Expertise :

- Bachelor's or master's degree in computer science, Information Systems, Data Science, or related field.

- 6+ years of experience in data architecture, data engineering, or a related field.

- Proficient in data pipeline tools such as Apache Spark, Kafka, Airflow, or similar.

- Experience with data governance frameworks and tools (e.g., Collibra, Alation, OpenMetadata).

- Strong knowledge of cloud platforms (Azure or Google Cloud), especially with cloud-native data services.

- Strong understanding of API design and data security best practices.

- Familiarity with data mesh, data fabric, or other emerging architectural patterns.

- Experience working in Agile or DevOps environments.

- Experience with modern data stack tools (e.g., dbt, Snowflake, Databricks).

- Extensive experience with high level programming languages - Python, Java or Scala

- Experience with Hive, Oozie, Airflow, HBase, MapReduce, Spark along with working knowledge of Hadoop/Spark Toolsets.

- Extensive Experience working with Git and Process Automation

- In depth understanding of relational database management systems (RDBMS) and Data Flow Development.

Soft Skills :

- Problem-Solving : Strong analytical skills to troubleshoot and resolve complex data pipeline issues.

- Communication : Ability to articulate technical concepts to non-technical stakeholders and document processes clearly.

- Collaboration : Experience working in cross-functional teams and managing stakeholder expectations.

- Adaptability : Willingness to learn new tools and technologies to stay ahead in the rapidly evolving data landscape.