Staff Data Platform Engineer

Remote from: Portugal
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Software Engineering
Employment type: Full Time,
Job posted: 5 Jun 2026
Apply before: 5 Jul 2026
Experience level: Senior
Views / Applies: 19 / 1

Leading provider of blockchain intelligence and compliance solutions empowering financial institutions, governmental agencies, and regulators.

Computer Software
2016

AI Summary

This Staff Data Platform Engineer role at Crystal Blockchain involves designing and evolving data pipeline architecture for blockchain data processing. The position requires 8+ years of experience, expert-level Python and SQL, and deep knowledge of modern data stacks like Kafka, Spark, and Flink. The engineer will mentor team members, drive technical direction, and take ownership of outcomes. It's a remote role open to candidates in Europe, with no prior blockchain knowledge required.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight This role demands 8+ years of experience, deep expertise in data pipelines, distributed systems, and mentorship, making it a highly challenging position.

Salary Analysis

AI Insight The offered salary is not specified, but for a Staff Data Platform Engineer with 8+ years of experience, the market median in the US is approximately $200k per year. This position is competitive for the level of expertise required.

Key Skills

Python SQL Apache Spark Apache Kafka Apache Flink ClickHouse Docker Kubernetes Terraform Data Pipelines

Cover Letter Sample

I am writing to express my interest in the Staff Data Platform Engineer position at Crystal Blockchain. With over 8 years of experience in data engineering, I have designed and scaled data pipelines using technologies like Kafka, Spark, and Flink, and I am proficient in Python and SQL. My background includes mentoring engineers and driving technical decisions that align with business goals.

I am particularly drawn to the challenges of blockchain data processing and the opportunity to work with a modern tech stack including ClickHouse, Iceberg, and Kubernetes. I am confident that my experience with distributed systems and data lakes will enable me to contribute effectively from day one.

I look forward to the possibility of discussing how my skills align with your team's needs. Thank you for considering my application.

Possible Interview Questions

Can you describe your experience designing and scaling data lakes or lakehouses at production scale?

I have designed and maintained a data lake on AWS S3 using Apache Iceberg, processing petabytes of data. I implemented partitioning and compaction strategies to optimize query performance and reduce costs.

How do you ensure data quality and reliability in streaming pipelines?

I use schema validation, dead letter queues, and idempotent sinks. I also implement monitoring with metrics like lag and partition health, and set up alerting for anomalies.

Explain a time you mentored a junior engineer on complex data engineering concepts.

I pair-programmed with a junior engineer on a complex stateful Flink job, explaining watermarks, checkpointing, and event time processing. Over time, they became independent and contributed to the same pipeline.

How do you approach trade-offs between performance and resource usage in data pipelines?

I evaluate based on latency requirements and cost constraints. For near-real-time pipelines, I might choose Kafka with minimal batching; for batch, I optimize for throughput using appropriate partitioning and compression.

Describe your experience with Kubernetes and Terraform in a data platform context.

I have deployed and managed Spark and Flink clusters on Kubernetes using Helm charts, and used Terraform to provision infrastructure as code for data storage and compute resources, ensuring reproducibility and scalability.

Position Summary:
We are looking for a talented Staff Data Engineer to join our Data Engineering team, to participate in the development of data pipelines for blockchain data processing. This is a remote role, and we are flexible with considering applications from anywhere in Europe.
More details: crystalblockchain.com
Duties and responsibilities:

Design and evolve data pipeline architecture and storage systems, driving technical direction across the team;
Build and deliver high-quality data pipelines end-to-end, from initial design through production deployment;
Mentor engineers, raise the technical bar through code reviews, and unblock teammates on complex problems;
Own team outcomes — set expectations, ensure delivery quality, and take accountability for results.

Required:

8+ years of hands-on software engineering experience, with significant depth in data pipelines, backend services, or data platform engineering;
Deep experience designing and operating data lakes/warehouses/lakehouses at production scale;
Experience in scaling data pipelines and good understanding of trade-offs (performance, resources)
Expert Python skills: you write clean, performant, testable code and can establish standards for others;
Experience across the modern data processing stack e.g., Kafka or Redpanda (real-time ingestion, delivery semantics), Flink or Spark (stream and batch processing, stateful operations), Airflow or Dagster (scheduling, backfills, dependency management);
Expert-level SQL: complex joins, query planning and optimization, schema design across both OLTP and OLAP systems;
Strong database architecture skills;
Solid distributed systems experience;
Comfortable leading technical discussions, building consensus on architectural decisions, and being the person the team turns to when things get hard;
Team-oriented work and good communication skills are an asset;
Proficiency in English.

Would be a plus:

Experience working with distributed Clickhouse cluster;
Blockchain domain knowledge or an ability of mastering complex technical domains quickly;
Track record of driving engineering standards and best practices across a team.

No previous knowledge of Blockchain technology required.

Tech stack: