Senior Data Engineer

Lviv, Kyiv

Open 3 months, first seen by us 95 days ago

Pay: Pay not listed
Work setting: Not stated
Employment: Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting

What you’ll work on

Full posting

Design and build batch pipelines that ingest and validate multi-billion-row datasets
Model temporal and slowly changing data and evolve models as upstream schemas change
Migrate pipelines from the Redshift warehouse to the Databricks lakehouse
Prove correctness with parity checks, reconciliation and large historical backfills
Build and maintain Python REST APIs that serve data to internal products

From the employer’s posting

Design and build batch data pipelines that ingest, validate and transform multi-billion-row datasets

Model complex real-world data: dimensional models, reference data, and temporal data whose attributes change over time (e.g. slowly changing dimensions)

including migrating existing pipelines from the warehouse to the lakehouse

design parity checks and reconciliation queries when replacing an existing pipeline, run large historical backfills

Build and maintain Python services and REST APIs that serve data to internal products

What you’ll bring

All qualifications

Core experience

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Ability to learn new things quickly, maintain a high bar for quality, and be pragmatic
Python is our primary language; SQL is everywhere
7+ years of work experience as a data engineer
Ability to communicate effectively with the U.S.-based teams and work 11:00 AM — 8:00 PM EEST (11:00 - 20:00).
Proficiency in Python and using it as the primary development language in recent years

Preferred experience

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

Qualification wording

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

Ability to learn new things quickly, maintain a high bar for quality, and be pragmatic

Python is our primary language; SQL is everywhere

7+ years of work experience as a data engineer

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

Proficiency in Python and using it as the primary development language in recent years

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

Education & alternatives

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

Apply at Simulmedia

Posting record

Report an error

Open 3 monthsWe first saw this posting 95 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records: Live
First seen by us: Aug 1, 202695 days ago
Recorded sightings: 15
Last seen by us: Nov 4, 2026
Employer says posted: Jul 30, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 95

Up longer than most. Similar jobs usually come down between day 6 and day 54.

From 10,845 similar postings that came down. Coming down is not always a hire.

Simulmedia is hiring

1 open job

No new data jobs since August

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
SQL
AWS
BigQuery
ClickHouse
Databricks
Delta Lake
Docker
Grafana
Redshift
S3
Snowflake
Spark
Airflow
FastAPI
Flask
PostgreSQL

Where the posting names them

Simulmedia is looking for an experienced and dynamic 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 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. Location: 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.

- 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

- 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

- 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

- 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

- 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)

- 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)

Our Tech Stack: - Almost everything we run is on AWS (S3, ECS, EMR, RDS and more) - Python is our primary language; SQL is everywhere

- 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)

- 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

- 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

- 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

- Airflow orchestrates our pipelines - Docker for packaging; GitHub Actions and Jenkins for CI/CD - Grafana, Sentry and OpenSearch for observability

- Docker for packaging; GitHub Actions and Jenkins for CI/CD - Grafana, Sentry and OpenSearch for observability - Datasets measured in billions of rows

- 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

- 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

- 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

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About the role

We file this job under Data Engineer, by the work the posting describes.

Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.

Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.

An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.

US government figures for database architects

Median yearly pay, May 2025: $139,500
Expected change, 2025 to 2035: 9.4% more jobsagainst 3.5% for all jobs

The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

Simulmedia is looking for an experienced and dynamic 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 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.

Location: 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
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).

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 OpenSearch for observability
Datasets measured in billions of rows

Interview Process:

Pre-screening (30 mins)
Technical Interview (1h)
System Design (1.5h)
Product Interview (30 mins)
Offer

Employment type

Full-time

Source details

View original posting ↗

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