Data Engineer

Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.

About the Product:

Our client is a major online gaming operator building a next-generation transactional platform for user account management. This is a greenfield project designed to operate in a highly regulated, high-volume environment and support millions of concurrent users. The platform will process financial transactions in real time and must ensure high availability, sub-second performance, data integrity, security, and full auditability. One of the key engineering challenges is maintaining reliability and performance during extreme traffic spikes and peak events.

The team is looking for a hands-on Data Engineer to help build and maintain scalable and reliable data infrastructure, develop production-grade data pipelines, and work with large volumes of business and transactional data, with a strong focus on data quality, integrity and performance.

About the Role:

You will contribute to the design, implementation, and operation of the data lake and lakehouse architecture, working closely with senior engineers on key technical decisions. This includes building and maintaining reliable data pipelines, working with large-scale business and transactional data, and supporting how data is stored, partitioned, processed, and monitored. You will work closely with Data Engineering, DevOps, Data Science, and other stakeholders to ensure data is accurate, reliable, and available for different use cases. You will also contribute to the data migration, help monitor data quality and pipeline health, and support the performance and long-term maintainability of the data infrastructure.

Key Responsibilities:

Build and maintain a multi-layer lakehouse (bronze/silver/gold) using open table formats such as Iceberg and Delta, including schema evolution and data organization

Build and support CDC and streaming ingestion pipelines using Kafka, Debezium, or similar technologies, including handling late and duplicate events

Work with cloud object storage and optimize data layout through partitioning, compaction, file sizing, and query performance improvements

Support data retention, archival, immutability, and GDPR-style deletion processes

Monitor data freshness and quality, identify data drift, and support reconciliation with source transactional systems

Implement and maintain data governance practices, including metadata, lineage, access controls, PII protection, encryption, and audit trails

Monitor pipeline and ingestion health, investigate data anomalies, and support reliable day-to-day operation of the data infrastructure

Collaborate with Data Engineering, DevOps, Data Science, Analytics, Finance, and Compliance teams on data requirements, migration, and delivery

Required Competence and Skills:

3+ years of hands-on data engineering experience working with large-scale data systems, data lakes, or lakehouse environments

Production experience with cloud object storage (AWS S3 or equivalent) and at least one analytics platform such as Snowflake, Databricks, or BigQuery

Hands-on experience with open table formats such as Iceberg, Delta, or Hudi, including a good understanding of schema evolution

Good understanding of partitioning, file layout, data storage, and query optimization for large datasets

Experience building or supporting CDC and streaming data pipelines using Kafka, Debezium, or similar technologies

Strong SQL and data modeling skills, with a good understanding of relational databases and transactional data

Understanding of data quality, monitoring, and governance concepts, including metadata, lineage, access control, PII handling, and retention

Hands-on experience with Python for data engineering tasks and familiarity with workflow orchestration tools such as Airflow or Dagster

Ability to work independently, take ownership of assigned tasks, and contribute to technical discussions and design decisions

Nice to Have:

Experience working with financial, transactional, or ledger data

Experience in regulated or audit-heavy environments such as fintech, gaming, banking, payments, or similar industries

Experience participating in large-scale data migration or building a new data platform