Senior Data Engineer

About the Role

We are seeking an experienced Data Engineer with strong big data engineering skills to join our growing team. You will design and implement large-scale, cloud-based data solutions that support advanced analytics, AI/ML, and BI initiatives. Your work will directly influence data-driven decision-making across the company. You’ll collaborate with data scientists, analysts, and product teams, ensuring our data is accurate, secure, and highly available. You’ll also help shape our data architecture as we continue to scale.

Key Responsibilities

• Architect, develop, and maintain large-scale, distributed ETL/ELT pipelines for batch and streaming workloads.
• Design and manage data lakes, warehouses, and lakehouses for analytics and machine learning use cases.
• Work with big data processing frameworks (Apache Spark, PySpark, Flink) for large-scale data transformation.
• Implement real-time streaming pipelines using Kafka, Kinesis, or equivalent technologies.
• Integrate data from varied sources including APIs, logs, IoT, and S3.
• Apply data governance, lineage tracking, and quality assurance best practices.
• Optimize performance for large datasets and ensure system scalability.
• Automate workflows using orchestration tools (Airflow, dbt, MageAI).
• Collaborate with stakeholders to define data solutions aligned with business needs.

• 6+ years in data engineering with demonstrable big data project experience.
• Proficient in Python, SQL, and distributed data processing (Spark/PySpark).
• Solid experience with AWS (S3, Glue, Athena, EMR, Lambda, Redshift).
• Knowledge of data modeling (star/snowflake), warehousing concepts, and NoSQL systems (MongoDB, DynamoDB, Cassandra).
• Familiarity with CI/CD, Git, and Infrastructure as Code (Terraform, CloudFormation).
• Experience in containerized environments (Docker, Kubernetes).