Senior Data Engineer

We are seeking an experienced Senior Data Engineer to design, build, and optimize scalable data pipelines and platforms. The ideal candidate has a deep understanding of data architecture, ETL/ELT processes, cloud technologies, and big data tools. You will work closely with analytics, data science, and product teams to ensure high-quality, reliable, and accessible data.Key ResponsibilitiesDesign, develop, and maintain robust, scalable ETL/ELT pipelines for batch and real-time data processing.Architect and implement data storage solutions, including data lakes, warehouses, and streaming systems.Build and optimize data models, ensuring performance, reliability, and ease of use.Collaborate with data scientists, analysts, and stakeholders to understand and provide data requirements.Ensure data quality, validation, lineage, governance, and documentation.Implement best practices for data security, access control, and compliance.Develop automation and monitoring for data workflows.Optimize performance of data processing jobs and distributed systems.Mentor junior team members and contribute to architectural decisions.Work with DevOps teams to deploy and manage data workloads in cloud environments.Required Skills & Experience5+ years of hands-on experience in data engineering.Strong experience with programming languages such as:Python, Scala, or Java.Proficiency in SQL and working with large datasets.Experience with big data frameworks such as:Apache Spark, Hadoop, Kafka, Flink, Airflow, dbt.Cloud expertise in at least one platform:AWS (Glue, EMR, Redshift, Athena, S3)Azure (Data Factory, Synapse, Databricks)GCP (BigQuery, Dataflow, Dataproc)Experience working with data warehouses:Snowflake, Redshift, BigQuery, Synapse, etc.Knowledge of CI/CD pipelines, Git, Terraform, Docker, or Kubernetes.Understanding of data modeling, dimensional modeling, and schema design.Strong grasp of distributed systems and performance optimization.Preferred Qualifications (Nice to Have)Experience with Databricks or Delta Lake.Knowledge of machine learning pipelines or feature stores.Hands-on experience with streaming systems (Kafka, Kinesis).Exposure to data governance frameworks (Collibra, Alation).Experience mentoring other engineers or leading technical initiatives