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Senior Data Engineer
Job Summary
We are seeking an experienced Senior Data Engineer to design, develop, and maintain scalable data pipelines and data platforms that support business intelligence, analytics, machine learning, and operational reporting. The ideal candidate will have strong expertise in data architecture, ETL/ELT processes, cloud platforms, and big data technologies, along with the ability to mentor junior engineers and collaborate with cross-functional teams.
Key Responsibilities Design, build, and optimize scalable data pipelines for structured and unstructured data. Develop and maintain ETL/ELT workflows to ingest, transform, and load data from multiple sources. Design and implement data models, data warehouses, and data lakes. Ensure data quality, integrity, governance, and security across the organization. Optimize database performance, query execution, and data processing workloads. Collaborate with data scientists, analysts, product managers, and business stakeholders to understand data requirements. Implement monitoring, alerting, and troubleshooting mechanisms for data pipelines. Build and maintain cloud-based data platforms and infrastructure. Lead data architecture discussions and recommend best practices. Mentor junior data engineers and participate in code reviews. Support machine learning and AI initiatives by preparing reliable and scalable datasets. Document data processes, standards, and technical solutions. Required Qualifications
Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or a related field. 5+ years of experience in data engineering or related roles. Strong proficiency in SQL and database design principles. Experience with Python, Scala, Java, or similar programming languages. Hands-on experience with ETL/ELT tools and frameworks. Strong knowledge of data warehousing concepts and dimensional modeling. Experience with cloud platforms such as AWS, Azure, or GCP. Expertise in distributed data processing technologies such as Spark, Hadoop, or Databricks. Experience working with relational and NoSQL databases. Knowledge of CI/CD pipelines, DevOps practices, and version control systems. Strong analytical, problem-solving, and communication skills. Preferred Qualifications
Experience with data orchestration tools such as Apache Airflow. Experience with streaming technologies such as Apache Kafka or Kinesis. Familiarity with containerization and orchestration tools (Docker, Kubernetes). Experience supporting machine learning workflows and MLOps. Cloud certifications (AWS, Azure, or GCP) are a plus. Technical Skills
Data Engineering
SQL Python Apache Spark Hadoop Databricks ETL/ELT Development Data Warehousing Cloud Platforms
AWS (Redshift, Glue, EMR, S3) Azure (Data Factory, Synapse, Data Lake) GCP (BigQuery, Dataflow, Cloud Storage) Databases
PostgreSQL MySQL SQL Server MongoDB Cassandra Tools & Technologies
Apache Airflow Kafka Docker Kubernetes Git Jenkins Terraform Preferred Experience
Building enterprise-scale data platforms. Supporting real-time and batch data processing systems. Working in Agile/Scrum environments. Leading technical projects and mentoring engineering teams. Key Competencies
Data Architecture System Design Performance Optimization Leadership & Mentoring Stakeholder Management Problem Solving Communication & Collaboration
Experience:
5-10+ Years Employment Type:
Full-Time Location:
Remote/Hybrid/Onsite
Languages
- English
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