Senior Data Engineer

The company is looking for an experienced and dynamic Senior Data Engineer with a curious and creative mindset to join our Data Services team. The ideal candidate will have a strong background in Python, SQL and large-scale data pipelines.

This is an opportunity to join a team of amazing engineers, data scientists, product managers and designers who are obsessed with building the most advanced TV and streaming advertising platform in the market.

As a Senior 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. You will work on a team that empowers the other teams to use our huge amount of data efficiently. Using a large variety of technologies and tools, you will solve complicated technical problems and build solutions to make our pipelines robust and fault tolerant and our data easily accessible throughout the company.

This is primarily a batch and warehouse role: most of the work is large Spark and SQL pipelines, data modeling, and migrating pipelines from Redshift to Databricks. Streaming is a small part of the job.

Ukraine is mandatory. Our offices are located in Kyiv and Lviv. Teams are located in Kyiv and Lviv and primarily work remotely with occasional offline meetings.

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

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

7+ years of work experience as a data engineer

Python as your primary language for pipelines and services in recent years

3+ years of hands-on Spark (PySpark) in production batch pipelines: you wrote the jobs, deployed them through CI/CD and kept them running at scale

Production experience with Databricks: Jobs/Workflows, Delta Lake, and deploying through CI/CD. Notebook-only experience does not count. A strong candidate without Databricks must show deep production PySpark on AWS EMR instead

Expert-level SQL: writing, reading and tuning complex analytical queries against very large tables, and debugging why two result sets disagree

Amazon Redshift in production is strongly preferred. Otherwise, deep hands-on tuning of another MPP columnar warehouse: distribution and sort keys, workload management, and diagnosing slow queries from the query plan

Built and owned production Airflow DAGs: dependencies, retries, backfills and alerting

Ability to design complex data models: normalized, dimensional and temporal (slowly changing dimensions, effective-dated records, point-in-time correctness)

Experience integrating third-party data feeds: 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 large server-side code bases

Working knowledge of AWS (S3, IAM, ECS) and Docker

Good engineering practices: unit and integration tests, code review, CI/CD

The desire to take a high level of ownership, learn quickly, keep a high bar for quality and be pragmatic

Able to communicate effectively with U.S.-based teams and work 11:00 AM – 8:00 PM EEST (11:00–20:00)

Nice to Have

Experience migrating legacy pipelines between platforms with strict parity requirements

Experience with advertising, media or measurement industry data

Our 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 VictoriaLogs for observability

Datasets measured in billions of rows

Interview Process

Pre-screening (30 mins)

Technical Interview (1h): we will go deep on a Spark pipeline you built and ran in production

System Design (1.5h)

Product Interview (30 mins)

Offer.