Senior Data Engineer

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
The company is a trailblazer in the world of data-driven advertising, known for its innovative approach to optimizing ad placements and campaign effectiveness through advanced analytics and machine learning techniques. Its mission is to revolutionize the advertising sector by enabling brands to reach their audiences more effectively.

About the role:
We arelooking for an experienced and dynamic Data Engineer with a curious and creative mindset to join our client’s team. The ideal candidate will have a strong background in Python, SQL and large-scale data pipelines. As a Data Engineer you will design, build and operate the data platform that powers the company: pipelines that ingest and transform very large datasets from external data partners, data models that the whole company queries, and services that make that data available to internal products.
Location: Ukraine is mandatory.

Responsibilities:
-Design and build batch data pipelines that ingest, validate and transform multi-billion-row datasets from external data providers and internal systems;
- Model complex real-world data: dimensional models, reference data, and temporal data whose attributes change over time (e.g. slowly changing dimensions), and evolve those models safely as upstream sources change their schemas and semantics;
- Develop and operate workloads on our lakehouse platform (Databricks / Spark / Delta) and our data warehouse (Redshift), including migrating existing pipelines from the warehouse to the lakehouse;
- Orchestrate pipelines with Airflow: scheduling, dependencies, retries, backfills and alerting;
- Prove correctness, not just completion: design parity checks and reconciliation queries when replacing an existing pipeline, run large historical backfills, and investigate data discrepancies down to the row level;
- Build and maintain Python services and REST APIs that serve data to internal products;
- Optimize for performance and cost: query tuning, table design, workload management and right-sizing compute;
- Own what you ship: monitor production pipelines, participate in incident triage and root-cause analysis, and harden systems so the same failure does not happen twice;
- Collaborate cross-functionally with product managers, data scientists and stakeholders across the company to deliver on product roadmap;
- Work within an Agile team that releases cutting-edge new features regularly;
- Take a high degree of ownership and freedom to experiment with new technologies to improve our software.

Requirements:
- Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience;
- 7+ years of work experience as a data engineer;
- Proficiency in Python and using it as the primary development language in recent years;
- Expert-level SQL: comfortable writing, reading and tuning complex analytical queries against very large tables, and debugging why two result sets disagree;
- Hands-on experience with a distributed data processing platform (Spark/Databricks strongly preferred; EMR, Snowflake or BigQuery also relevant) and with a columnar data warehouse (Redshift, Snowflake, BigQuery, ClickHouse, etc);
- Ability to design complex data models: normalized, dimensional and temporal (slowly changing dimensions, effective-dated records, point-in-time correctness);
- Experience with workflow orchestration tools (Airflow or similar): building DAGs, managing dependencies and running backfills;
- Experience integrating third-party data feeds: handling schema drift, late or missing deliveries, vendor data-quality defects and versioned reference data;
- Experience building REST services in Python (FastAPI, Flask, etc);
- Experience developing, maintaining, and debugging problems in large server-side code bases;
- Working knowledge of AWS (S3, IAM, ECS or similar compute) and Docker;
- Good knowledge of engineering best practices and testing (unit test, integration test, code review, CI/CD);
- The desire to take a high level of ownership of the things you work on;
- Ability to learn new things quickly, maintain a high bar for quality, and be pragmatic;
- Must be able to communicate with U.S based teams;
- Experience with Delta Lake / medallion lakehouse architectures is a plus;
- Experience migrating legacy pipelines between platforms with strict parity requirements is a plus;
- Experience with advertising, media or measurement industry data is a plus;
- Ability to communicate effectively with the U.S.-based teams and work 11:00 AM — 8:00 PM EEST (11:00 — 20:00).

Tech Stack:
- Almost everything we run is on AWS (S3, ECS, EMR, RDS and more)
- Python is our primary language; SQL is everywhere;
- Databricks (Spark, Delta Lake) is our lakehouse platform; - Redshift and Postgres are our warehouses and operational databases;
- Airflow orchestrates our pipelines;
- Docker for packaging; GitHub Actions and Jenkins for CI/CD;
- Grafana, Sentry and OpenSearch for observability;
- Datasets measured in billions of rows.