Senior Data Engineer
Summary
Senior Data Engineer at Tikal working on client projects with high-scale, high-traffic data systems: building and orchestrating data pipelines, managing data lakes with Medallion Architecture and DBT, and ensuring scalability, security, and governance. Core stack includes Python, SQL, streaming (Spark/Flink/Kafka), NoSQL stores, and cloud platforms (AWS/GCP).
In your role as a Data Engineer at Tikal, you'll be actively engaged in the most fascinating and demanding projects for our clients. Handling high-scale operations, heavy traffic, stringent SLAs, and massive data volumes is just the starting point. Our leadership in the realms of Big Data, Streaming Data, and Complex Event Processing (CEP) stems from our adept use of cutting-edge, optimal technologies.
Responsibilities:
- Apply software-engineering methods and best practices (Medallion Architecture) in Data Lake management, using tools like DBT.
- Develop Data pipelines to ingest and transform data.
- Manage and enhance orchestrated pipelines across company
- Data Modeling: Developing data models to ensure efficient storage, retrieval, and processing of data, often involving schema design for NoSQL databases, data lakes, and data warehouses. This will also include catalogs of data.
- Scalability: Ensuring that the big data architecture can scale horizontally to handle large volumes of data and high traffic loads.
- Data Security: Implementing security measures to protect sensitive data, including encryption, access controls, and data masking.
- Data Governance: Establishing data governance policies to ensure data quality, compliance with regulations, and data lineage.
Requirements
- 7 years of experience in Python (+Java / Scala)
- Proven experience working in an AI-assisted development environment, defining high-level technical goals and leveraging autonomous AI agents (e.g., Cursor, Copilot, Devin-style tools) to execute multi-file implementations, while maintaining ownership of architecture, design decisions, code quality, and final validation.
- Experience in Building data lakes, including ingestion and transformation (DBT)
- Data Warehousing & ETL Pipelines
- SQL & Advanced Query Optimization
- Experience in data formats (Parquet, Avro, ORC)
- Experience in streaming (Spark Streaming, Flink, Kafka Streams, Beam, etc.)
- Experience in NoSQL and data storage (e.g. Elastic, Redis, MongoDB, CouchBase, BigQuery, Snowflake, Databricks)
- Experience in messaging (Kafka, RabbitMQ, etc.)
- Experience in cloud platforms (AWS, GCP)