Senior Data Engineer

Posted Yesterday
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Hiring Remotely in Israel
Remote or Hybrid
Senior level
Information Technology • Machine Learning • Software • Consulting
The Role
Build and manage scalable data lakes, warehouses, ETL pipelines, and orchestrated ingestion and transformation workflows. Develop data models, optimize SQL queries, support streaming and high-volume architectures, and implement data security, governance, quality, compliance, and lineage. The role requires ownership of architecture and code quality while using AI-assisted development tools to deliver complex multi-file implementations.
Summary Generated by Built In
Description

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)

Skills Required

  • 7 years of experience with Python, plus Java or Scala
  • Experience working in an AI-assisted development environment using autonomous AI agents such as Cursor, Copilot, or Devin-style tools
  • Experience building data lakes, including ingestion and transformation with dbt
  • Experience with data warehousing and ETL pipelines
  • SQL and advanced query optimization experience
  • Experience with Parquet, Avro, and ORC data formats
  • Experience with streaming technologies such as Spark Streaming, Flink, Kafka Streams, or Beam
  • Experience with NoSQL and data storage technologies including Elasticsearch, Redis, MongoDB, Couchbase, BigQuery, Snowflake, or Databricks
  • Experience with messaging technologies such as Kafka or RabbitMQ
  • Experience with cloud platforms including AWS or GCP
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The Company
150 Employees
Year Founded: 1999

What We Do

Tikal is a technology consultancy and development-services company serving early-stage startups and large international product companies. It supports clients through challenging stages of software development, including new projects, technology replacement, adoption of new technologies, and complex technical problems. Its core expertise spans backend, frontend, DevOps, mobile, and machine learning, with project-based engagement that typically ends after maintenance and bug-fixing begin.

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