Jobs Data Engineer

Project Description

We are a global audience and location intelligence company that helps marketers connect the digital and physical world. We provide data-driven solutions to enhance marketing campaigns by leveraging location and audience data to reveal consumer behavior and enable more precise targeting and measurement. We work on high-end / high-performance / high-throughput systems for in-time analysis of data for autonomous driving and other big data applications e.g. for E-commerce.

Job Responsibilities

Work in a self-organised agile team with a high level of autonomy, and you will actively shape your team's culture.

Design, build, and standardise privacy-first big data architectures, large-scale data pipelines, and advanced analytics solutions in AWS.

Develop complex integrations with third-party partners, transferring terabytes of data.

Align with other Data experts on data (analytics) engineering best practices and standards, and introduce those standards and data engineering expertise to the team in order to enhance existing data pipelines and build new ones.

Successfully partner up with the Product team to constantly develop further and improve our platform features.

Requirements:

• 4+ years in a Data Engineering or similar high-scale role.
• Expert-level Python skills. Familiarity with Java or Scala is a major plus.
• Proven hands-on experience building scalable solutions directly within AWS.
• Deep proficiency in high-throughput architectures using Kafka, Spark, Hadoop, MongoDB, AWS Batch, AWS Glue, Athena, Airflow, and dbt.
• Exceptional SQL and data transformation skills, with an eye for optimizing NoSQL and SQL databases.
• Ability to simplify complex technical concepts and a passion for mentoring junior engineers in a collaborative environment.

We build and operate AI-enabled systems that create measurable operational impact across the organization. Our primary output is working software in production.

Anchors the data platform architecture and owns enterprise data extraction end-to-end.

Ideal Candidate

Has designed and built a data platform from scratch in an enterprise environment — not inherited a running system, built one. Has pulled data from SAP or comparable ERP into a warehouse and survived upstream failures without silent data loss. Sets standards that other engineers can follow without three clarification meetings. Can make architecture calls quickly and correct them later. Has strong opinions about pipeline observability and data quality.

Skills Required

• SAP data extraction: OData services, RFC/BAPI, SAP Data Services, or CDS views — hands-on experience pulling data from SAP FI/CO modules
• Python — async data pipelines, background jobs, scheduled tasks
• PostgreSQL — schema design, migrations (Alembic), query optimization, partitioning
• Azure Data Lake Storage + Synapse Analytics
• Microsoft Graph API — SharePoint, M365, organizational data
• Apache Airflow or Azure Data Factory
• dbt or equivalent for transformation and quality testing
• Redis — queue management, TTL, cache invalidation
• Observability — structured logging, Azure Monitor or Prometheus/Grafana
• Docker — containerized pipeline jobs
• Infrastructure-as-code (Terraform or equivalent)

Skills Required

• SAP data extraction: OData services, RFC/BAPI, SAP Data Services, or CDS views — hands-on experience pulling data from SAP FI/CO modules
• Python — async data pipelines, background jobs, scheduled tasks
• PostgreSQL — schema design, migrations (Alembic), query optimization, partitioning
• Azure Data Lake Storage + Synapse Analytics
• Microsoft Graph API — SharePoint, M365, organizational data
• Apache Airflow or Azure Data Factory
• dbt or equivalent for transformation and quality testing
• Redis — queue management, TTL, cache invalidation
• Observability — structured logging, Azure Monitor or Prometheus/Grafana
• Docker — containerized pipeline jobs
• Infrastructure-as-code (Terraform or equivalent)
  
  Has designed and built a data platform from scratch in an enterprise environment — not inherited a running system, built one. Has pulled data from SAP or comparable ERP into a warehouse and survived upstream failures without silent data loss. Sets standards that other engineers can follow without three clarification meetings. Can make architecture calls quickly and correct them later. Has strong opinions about pipeline observability and data quality.

Join the Data Engineering team to contribute to the ongoing maintenance and improvement of an internal LLM-powered assistant that uses hosted LLM APIs and internal knowledge sources, with a focus on reliability, retrieval quality, and operational excellence.

Responsibilities

Maintain and enhance ingestion/enrichment pipelines for internal content (parsing/extraction, normalization, metadata enrichment, deduplication, and quality monitoring)

Improve indexing and retrieval performance and quality (chunking/segmentation refinements, embedding/index update workflows, metadata filtering, caching) and support hybrid retrieval capabilities (vector + keyword/BM25 + metadata)

Implement and maintain access-aware retrieval by propagating/enforcing document permissions through indexing and query-time filters, including audit logs and validation tests

Improve source attribution so responses reliably point to the correct documents and sections in a consistent format.

Extend and harden tool/workflow execution and automations (scheduled/trigger-based), including retries, timeouts, idempotency, concurrency controls, and run history

Develop and maintain evaluation and regression testing (golden sets, automated scoring) and support structured comparisons across LLM providers/models as required

Operate the platform in production: observability (logs/metrics/tracing), alerting, incident support, performance tuning, and cost controls, plus runbooks and handover documentation

Skills

Must have

8+ years of hands-on experience in Data Science and 5+ years in Machine Learning, with a proven track record, demonstrated through a robust portfolio of projects.

Strong programming skills in languages such as Python and familiarity building ETL pipelines.

Expertise in SQL and experience with both relational (preferably Postgres) and NoSQL databases (Open Search or Elastic Search)

Familiarity with cloud platforms (e.g., AWS) and their services.

Experience with version control systems (e.g., Git) and CI/CD pipelines.

Ability to build scalable infrastructure to embed and search very large number of documents.

Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines.

Expertise in ML inference optimizations

Solid experience with Hybrid RAG, chunking/segmentation refinements, embedding/index update workflows, metadata filtering, caching, etc.

Knowledge of network optimization for distributed ML training and inference.

Understanding of distributed training patterns and checkpointing strategies.

Strong English skills (B2 and higher)

Strong verbal and written communication skills.

Ability to work independently and collaborate in a group.

Nice to have

Agile certification

AWS/Oracle/Microsoft attestations and certifications

Domain knowledge

Trading and Capital Markets

Job Description

• Expertise in building data ingestion, ETL/ELT pipelines and orchestration on AWS
• Experience with Change Data Capture (CDC) and real-time streaming data architectures
• Experience with SQL and data modeling
• Understanding of distributed systems and big data processing
  Python for data transformations and scripting

• Hands-on experience with orchestration frameworks like AWS Step Functions or Data Pipeline

  Nice to Have:

• Experience with data quality frameworks and monitoring
• Knowledge of HIPAA or similar healthcare data compliance standards
• Experience working in highly regulated environments
• Amazon Web Services (AWS) – Kinesis, DMS, EMR, Glue, Lambda, Athena, Redshift, S3

Job Responsibilities

• Design and development of scalable and reliable data pipelines in AWS using tools such as Kinesis, DMS, Glue, EMR, Athena, and Lambda.
• Define the architecture of the data ingestion and processing layer, considering performance, cost-efficiency, and maintainability.
• Collaborate closely with business stakeholders and technical teams to gather requirements, validate solutions, and align data flows with business needs.
• Propose and validate technical approaches and best practices for CDC, ETL/ELT, orchestration, data storage, and query optimization.
• Own the implementation of key data workflows, ensuring quality, security, and adherence to architectural standards.
• Conduct technical reviews, mentor junior engineers, and foster a culture of continuous improvement.
• Participate in the strategic planning of the data platform and actively contribute to its evolution.
• Monitor and optimize performance of AWS-based data solutions, addressing bottlenecks and proposing enhancements.

Department/Project Description

The client is a pioneer in medical devices for less invasive surgical procedures, ranking as a leader in the market for coronary stents. The company’s medical devices are used in a variety of interventional medical specialties, including interventional cardiology, peripheral interventions, vascular surgery, electrophysiology, neurovascular intervention, oncology, endoscopy, urology, gynecology, and neuromodulation.
The client’s mission is to improve the quality of patient care and the productivity of health care delivery through the development and advocacy of less-invasive medical devices and procedures. This is accomplished through the continuing refinement of existing products and procedures and the investigation and development of new technologies that can reduce risk, trauma, cost, procedure time and the need for aftercare.

PwC is a global network of more than 370,000 professionals in 149 countries that turns challenges into opportunities. We create innovative solutions in audit, consulting, tax and technology, combining knowledge from all over the world.

PwC SDC Lviv, opened in 2018, is part of this global space. It is a place where technology is combined with team spirit, and ambitious ideas find their embodiment in real projects for Central and Eastern Europe.

What do we guarantee?

• Work format: Remote or in a comfortable office in Lviv - you choose.
• Development: Personal development plan, mentoring, English and Polish language courses.
• Stability: Official employment from day one, annual review of salary and career prospects.
• Corporate culture: Events that unite the team and a space where everyone can be themselves.

We are looking for a talented and experienced Cloud Engineer to join our growing team. As well as experience with cloud infrastructure, networking, and security, the Cloud Platform Engineer will be responsible for operating, monitoring, and maintaining our many Azure subscriptions and associated global applications. As a Cloud Platform Engineer with cloud architecture, you will work closely with other data professionals, software engineers, and business stakeholders to build, maintain and support efficient, scalable, and secure data pipelines. This role involves both engineering and operations, requiring comfort with project work and handling incident tickets as part of the team.

Responsibilities: 

• Design, build and maintain Azure based infrastructure with Terraform modules (IaaS and PaaS resources) as part of new projects or updates needed to production environments;
• Troubleshoot IaaS and PaaS issues raised via SNow incident and meet ticket closure SLA’s;
• Documentation and knowledge sharing: create and maintain environment diagrams, playbooks, runbooks, and best practice documents;
• Communicate with internal business partners including guidance on application design.

Qualifications:

• 2+ years of experience with Azure core services: Resource Manager, Virtual Machines/Scale Sets, Storage (Blob/Files), Networking (VNets, NSGs, Load Balancer, App Gateway, DNS, Private Link);
• 2+ year of experience in Windows or Linux administration, with strong problem-solving abilities;
• 2+ years of experience with Azure Kubernetes Service;
• 2+ years of experience with Terraform;
• Ability to work independently and initiate tasks without supervision and utilize strong interpersonal and organizational skills;
• Excellent problem-solving and analytical skills.

Policy statements:
https://www.pwc.com/ua/uk/about/privacy.html

About the Product

We are building a US-based, data-driven product with a strong focus on scalability, performance, and cost efficiency.

Our mission is to design a modern data platform that transforms raw behavioral and monetization data into reliable, actionable business insights — in near real-time.

For us, data engineering is not just about moving data.
It’s about:

• Designing resilient architecture
• Optimizing for performance and cost
• Building reliable automation
• Ensuring architectural integrity at scale
•  

Role Overview

We are looking for a Senior Data Engineer who will take ownership of the data platform architecture and drive technical excellence across ingestion, modeling, and performance optimization.

This role requires deep expertise in SQL, Python, AWS infrastructure, and modern data stack principles. You will not only build pipelines — you will define standards, lead architectural decisions, and proactively improve system efficiency.

You will play a critical role in ensuring that data flows seamlessly from event streams to business-ready datasets while maintaining high performance, reliability, and cost control.

What Makes This Role Senior-Level

As a Senior Data Engineer, you will:

• Own architectural decisions for the data platform
• Identify scalability bottlenecks before they become incidents
• Optimize data infrastructure for performance and cost
• Lead technical code reviews and set engineering standards
• Mentor mid-level engineers
• Act as a technical partner to Product and Analytics stakeholders
• Balance real-time and batch processing strategies strategically

Technical Requirements

Must-Have

Expert-Level SQL

• Complex analytical queries and window functions
• Query optimization and execution plan analysis
• Identifying and eliminating performance bottlenecks
• Reducing query complexity and compute costs
• Designing partitioning and clustering strategies

Python

• Advanced data manipulation
• Building scalable ETL/ELT frameworks
• Writing production-grade data services
• Automation and monitoring scripts

AWS Core Infrastructure

• AWS Kinesis Firehose (near-real-time data streaming)
• Amazon S3 (data lake architecture and storage optimization)
• Designing reliable ingestion layers

Version Control

• Git (GitHub / GitLab)
• Branching strategies
• Leading technical code reviews
• Enforcing best practices in code quality

Nice-to-Have

Modern Data Stack

• dbt (modular SQL modeling, documentation, testing)
• Experience structuring layered data models (staging → intermediate → marts)

Data Warehousing

• Google BigQuery
• Slot management
• Cost-efficient querying
• Storage and compute optimization

Advanced Optimization Techniques

• Partitioning
• Clustering
• Bucketing
• Storage layout optimization

Integrations & Infrastructure

• Salesforce data integration
• Docker / ECS
• CI/CD for data workflows

AI / ML Exposure

• Supporting feature pipelines
• Understanding data requirements for ML systems

Key Responsibilities

Data Platform Architecture

• Design and maintain a scalable real-time and batch data platform
• Architect ingestion pipelines using AWS Kinesis and Python
• Ensure high availability and reliability of data flows

Real-Time Processing

• Enable near-real-time (seconds–minutes latency) data processing
• Build systems for operational alerting and anomaly detection
• Ensure early detection of monetization and traffic issues

Data Modeling

• Transform raw event data into business-ready datasets using dbt
• Design scalable, maintainable schemas aligned with product evolution

Performance & Cost Engineering

• Optimize SQL queries and storage structures
• Design cost-efficient partitioning strategies
• Monitor and reduce warehouse and infrastructure costs
• Balance real-time and batch processing appropriately

Engineering Excellence

• Lead and participate in code reviews
• Enforce high standards of performance, security, and maintainability
• Improve observability and monitoring across pipelines

Cross-Functional Collaboration

• Work closely with Data Analysts and Product Managers
• Translate business requirements into scalable technical solutions
• Clearly communicate trade-offs between speed, cost, and complexity

Type of Data We Process

• User behavior events (page views, clicks, searches, conversions)
• Ad & monetization events (impressions, clicks, CTR, attribution)
• System and integration logs (latency, errors, rate limits)

Why Real-Time Is Critical

• Detect broken ads or impression drops before revenue is lost
• Identify traffic anomalies or abuse early
• Enable same-day operational intervention
• Prevent negative user and advertiser experience

Near-real-time (seconds to minutes latency) is required for operational awareness.
Batch processing remains important for historical analysis and reporting — but not for incident detection.

Working Schedule

• Monday – Friday
• 16:00 – 00:00 Kyiv time
• Full alignment with US-based stakeholders

What We Value

• Strong ownership mindset
• Strategic thinking about architecture
• Focus on scalability, reliability, and cost efficiency
• Proactive problem-solving
• Clear communication with both technical and non-technical teams
• Ability to think beyond “just making it work”

PwC is a global network of more than 370,000 professionals in 149 countries that turns challenges into opportunities. We create innovative solutions in audit, consulting, tax and technology, combining knowledge from all over the world.

PwC SDC Lviv, opened in 2018, is part of this global space. It is a place where technology is combined with team spirit, and ambitious ideas find their embodiment in real projects for Central and Eastern Europe.

What do we guarantee?

• Work format: Remote or in a comfortable office in Lviv - you choose.
• Development: Personal development plan, mentoring, English and Polish language courses.
• Stability: Official employment from day one, annual review of salary and career prospects.
• Corporate culture: Events that unite the team and a space where everyone can be themselves.

PwC is seeking an experienced Databricks Platform Engineer to accelerate the maturation of our Databricks platform as a core component of the firm’s Data Mesh and AI-first strategy. This role will help shape the architectural direction, governance model, and enterprise standards that underpin secure and scalable Databricks adoption.

Role Overview

The Databricks Platform Engineer  will be responsible for executing against this strategy by driving service optimization, implementing advanced platform capabilities, and partnering with Product and Engineering teams to enable enterprise-scale adoption across client and corporate use cases.

Key Responsibilities:

• Lead the rollout and optimization of Databricks services at the PwC account level.
• Implement advanced capabilities including Unity Catalog, AI/ML models, Delta Sharing, Automatic Identity Management (AIM/SCIM), and Budget Policies for cost governance and tagging.
• Collaborate with Databricks and PwC platform teams to enhance and enable secure data exchange.
• Support networking design and optimization across Databricks environments.
• Partner with Product and Engineering teams to refine user experience, service provisioning models, and upcoming Databricks capabilities.
• Coordinate cross-functional efforts to enable AI-driven workflows and advanced analytics capabilities within Databricks.

Required Qualifications:

• 5+ years of experience in data engineering, cloud data platforms, or platform architecture.
• 3+ years of hands-on experience with Databricks or comparable modern data platforms.
• Expertise in Unity Catalog, Delta Sharing, Compute (classic and serverless), Microsoft Entra ID (SCIM/AIM), and Azure networking.
• Strong understanding of data governance, access control models, and FinOps best practices.
• Excellent communication skills with the ability to translate complex technical solutions into business value.

Preferred Qualifications:

• Databricks Certified Professional or Azure Databricks Certification.
• Hands-on experience with Databricks, Microsoft Fabric / OneLake, or other enterprise data platforms.
• Prior consulting experience or enterprise-scale platform transformation experience.

Policy statements:
https://www.pwc.com/ua/uk/about/privacy.html

PwC is a global network of more than 370,000 professionals in 149 countries that turns challenges into opportunities. We create innovative solutions in audit, consulting, tax and technology, combining knowledge from all over the world.

Join PwC’s pioneering data & AI team and help build the data foundations behind impactful AI solutions. We’re growing quickly due to a strong pipeline of client work, and we’re hiring across multiple seniority levels — from junior to senior Data Engineers and Data Scientists.

Across our projects, Python is the core skill we expect. Depending on your strengths, you may focus more on data engineering (pipelines, platforms, SQL) or data architecture (designing scalable data solutions). Many roles also include “full-solver” flexibility — contributing where needed, including automation, integration work, or enabling AI/GenAI use cases on modern platforms (including Microsoft technologies where relevant).

Key responsibilities:

• Data Pipeline Design & Development: Design, implement, and maintain scalable data pipelines and ETL/ELT processes, primarily using Python and Spark (PySpark), to ingest, transform, and deliver data from various sources into analytics and ML platforms.
• Data Modelling & Warehousing: Design and optimize data models (e.g. star/snowflake schemas), build and manage data warehouses and data lakes, and ensure data structures support reporting, analytics, and ML use cases.​
• Data Preparation for ML: Collaborate closely with data scientists and ML engineers to understand data requirements, implement robust preprocessing and feature engineering steps, and ensure datasets are clean, consistent, and suitable for machine learning models.
• Performance & Reliability: Optimize data processing jobs and SQL queries for performance and cost efficiency, monitor data pipelines in production, and ensure reliability, scalability, and adherence to SLAs.
• Client Collaboration: Work directly with clients across Western Europe and the USA to understand their data landscape and requirements, translate business needs into technical designs, and provide expert guidance on data architecture and best practices.
• Governance, Quality & Security: Implement data quality checks, validation frameworks, and governance standards; ensure data security, privacy, and compliance in line with PwC and client requirements.
• Learning & Development: Stay at the forefront of data engineering, big data, and cloud technologies, continuously improving existing solutions, tools, and processes.
• Mentorship: Support the growth of junior team members by sharing knowledge, reviewing code, and guiding them in data engineering best practices and project work.

Who We're Looking For:

• Programming Skills (Key Requirement)
  Strong programming skills in Python (e.g. pandas, PySpark, SQLAlchemy, airflow-like tools). Ability to write clean, maintainable, and testable code is essential. Experience with other programming languages is a plus.
• Demonstrated hands-on experience with building data pipelines, ideally using Spark (PySpark) or similar distributed processing frameworks.
• Designing and implementing ETL/ELT workflows.
• Working with large datasets and complex data transformations.
• Database Expertise: Proficiency in SQL databases (designing schemas, writing complex queries, optimization). 
• Cloud & Big Data (Nice to Have / Advantage): Experience with cloud data platforms (preferably Azure: Synapse, Databricks, Data Factory, Azure SQL, Data Lake) or similar services on AWS/GCP.
• Professional Background: At least 2 years of relevant professional experience in data engineering, BI engineering, or similar data-focused roles.
• Analytical Thinking: Strong analytical mindset with the ability to understand complex data landscapes, debug data issues, and design logical, efficient data flows.
• Language Skills: English at B2 level or higher.

Policy statements:
https://www.pwc.com/ua/uk/about/privacy.html

Project Description:

Join the Data Engineering team to contribute to the ongoing maintenance and improvement of an internal LLM-powered assistant that uses hosted LLM APIs and internal knowledge sources, with a focus on reliability, retrieval quality, and operational excellence.

Responsibilities:

• Maintain and enhance ingestion/enrichment pipelines for internal content (parsing/extraction, normalization, metadata enrichment, deduplication, and quality monitoring)
• Improve indexing and retrieval performance and quality (chunking/segmentation refinements, embedding/index update workflows, metadata filtering, caching) and support hybrid retrieval capabilities (vector + keyword/BM25 + metadata)
• Implement and maintain access-aware retrieval by propagating/enforcing document permissions through indexing and query-time filters, including audit logs and validation tests
• Improve source attribution so responses reliably point to the correct documents and sections in a consistent format.
• Extend and harden tool/workflow execution and automations (scheduled/trigger-based), including retries, timeouts, idempotency, concurrency controls, and run history
• Develop and maintain evaluation and regression testing (golden sets, automated scoring) and support structured comparisons across LLM providers/models as required
• Operate the platform in production: observability (logs/metrics/tracing), alerting, incident support, performance tuning, and cost controls, plus runbooks and handover documentation

Mandatory Skills Description:

8+ years of hands-on experience in Data Science and 5+ years in Machine Learning, with a proven track record, demonstrated through a robust portfolio of projects.
Strong programming skills in languages such as Python and familiarity building ETL pipelines.
Expertise in SQL and experience with both relational (preferably Postgres) and NoSQL databases (Open Search or Elastic Search)
Familiarity with cloud platforms (e.g., AWS) and their services.
Experience with version control systems (e.g., Git) and CI/CD pipelines.
Ability to build scalable infrastructure to embed and search very large number of documents.
Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines.
Expertise in ML inference optimizations
Solid experience with Hybrid RAG, chunking/segmentation refinements, embedding/index update workflows, metadata filtering, caching, etc.
Knowledge of network optimization for distributed ML training and inference.
Understanding of distributed training patterns and checkpointing strategies.
Strong English skills (B2 and higher)
Strong verbal and written communication skills.
Ability to work independently and collaborate in a group.

Nice-to-Have Skills Description:

Agile certification
AWS/Oracle/Microsoft attestations and certifications
Domain knowledge - Trading and Capital Markets

We are looking for a Senior Data Engineer. This is not a standard ETL role. You will work on a high-load system processing 10,000+ executions per minute, building scalable pipelines where AI and LLM logic are embedded directly into production data flows.

The role combines data engineering, backend thinking, infrastructure ownership, and real production AI integration.

What you will do:

• Design and maintain scalable data pipelines in Python on Google Cloud Platform
• Build and optimize AI-driven scoring flows using LLMs for real-time identity enrichment
• Work with BigQuery, Apache Spark, and Apache Iceberg for heavy data processing
• Maintain high-load orchestration with custom Python nodes and complex workflows
• Own deployment and release processes (CI/CD)
• Optimize PostgreSQL performance under heavy load
• Investigate incidents and resolve root causes permanently

• Use AI engineering tools such as Claude in daily development

   

Requirements:

• 4+ years of experience in Data Engineering / Backend Engineering
• Strong Python skills
• Commercial experience with production LLM integration (OpenAI / Anthropic / Gemini)
• Strong knowledge of GCP (Cloud Functions, IAM, Networking)
• Experience with BigQuery, Spark, Iceberg
• Good understanding of Kubernetes
• Experience with workflow orchestration tools (n8n is a strong plus)
• Strong practical knowledge of PostgreSQL

• English level B2+

   

What is important for us:

• Ownership mindset
• Fast learning ability
• Strong problem-solving approach
• Active AI usage in engineering work
• Focus on practical results over unnecessary complexity

Join a Company That Invests in You

Seeking Alpha is the world’s leading community of engaged investors. We’re the go-to destination for investors looking for actionable stock market opinions, real-time market analysis, and unique financial insights. At the same time, we’re also dedicated to creating a workplace where our team thrives. We’re passionate about fostering a flexible, balanced environment with remote work options and an array of perks that make a real difference.

Here, your growth matters. We prioritize your development through ongoing learning and career advancement opportunities, helping you reach new milestones. Join Seeking Alpha to be part of a company that values your unique journey, supports your success, and champions both your personal well-being and professional goals.

What We're Looking For

Seeking Alpha is looking for a Senior Data Engineer responsible for designing, building, and maintaining the infrastructure necessary for analyzing large data sets. This individual should be an expert in data management, ETL (extract, transform, load) processes, and data warehousing and should have experience working with various big data technologies, such as Hadoop, Spark, and NoSQL databases. In addition to technical skills, a Senior Data Engineer should have strong communication and collaboration abilities, as they will be working closely with other members of the data and analytics team, as well as other stakeholders, to identify and prioritize data engineering projects and to ensure that the data infrastructure is aligned with the overall business goals and objectives.

The Senior Data Engineer will be engaged with the company as a contractor (Private Entrepreneur).

What You'll Do

• Work closely with data scientists/analytics and other stakeholders to identify and prioritize data engineering projects and to ensure that the data infrastructure is aligned with business goals and objectives
• Design, build and maintain optimal data pipeline architecture for extraction, transformation, and loading of data from a wide variety of data sources, including external APIs, data streams, and data stores. 
• Continuously monitor and optimize the performance and reliability of the data infrastructure, and identify and implement solutions to improve scalability, efficiency, and security
• Stay up-to-date with the latest trends and developments in the field of data engineering, and leverage this knowledge to identify opportunities for improvement and innovation within the organization
• Solve challenging problems in a fast-paced and evolving environment while maintaining uncompromising quality.
• Implement data privacy and security requirements to ensure solutions comply with security standards and frameworks.
• Enhance the team's dev-ops capabilities.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field
• 2+ years of proven experience developing large-scale software using an object-oriented or functional language.
• 5+ years of professional experience in data engineering, focusing on building and maintaining data pipelines and data warehouses
• Strong experience with Spark, Scala, and Python, including the ability to write high-performance, maintainable code
• Experience with AWS services, including EC2, S3, Athena, Kinesis/Firehose Lambda and EMR
• Familiarity with data warehousing concepts and technologies, such as columnar storage, data lakes, and SQL
• Experience with data pipeline orchestration and scheduling using tools such as Airflow
• Strong problem-solving skills and the ability to work independently as well as part of a team
• High-level English - a must. 
• A team player with excellent collaboration skills.

Nice to Have:

• Expertise with Vertica or Redshift, including experience with query optimization and performance tuning
• Experience with machine learning and/or data science projects
• Knowledge of data governance and security best practices, including data privacy regulations such as GDPR and CCPA.
• Knowledge of Spark internals (tuning, query optimization)

Our client is seeking a motivated and skilled Data Engineer to join their core Data Engineering team. This is a hands-on role designed for a technical expert who enjoys building robust data architectures and working directly with the Modern Data Stack

As a Data Engineer, you will be responsible for designing, developing, and optimizing data transformation pipelines. You will play a crucial role in turning raw data into actionable insights by leveraging Snowflake and dbt to ensure high-quality, scalable data delivery.
 

Key Responsibilities:

• Design and build sophisticated SQL models to process both batch and near real-time data streams.
• Utilize dbt to implement reliable, version-controlled transformation logic.
• Ensure high performance and cost-efficiency within the Snowflake environment.
• Build and maintain scalable ELT/ETL processes to support the organization's evolving data needs.

Skills & Experience:

• Proven experience working with Snowflake architecture (Virtual Warehouses, Snowpipe, etc.).
• Strong proficiency in using dbt for modular data modeling and testing.
• Expert-level ability to write complex, performant, and clean SQL code.
• Solid understanding of data warehousing concepts, schema design (Star, Snowflake, Data Vault), and data lifecycle management.
• A proactive approach to problem-solving and a passion for deep-diving into technical challenges.

Nice to Have:

• Real-time Processing: Previous experience building SQL models specifically for real-time data pipelines.
• Experience with orchestration tools (e.g., Airflow, Dagster) or cloud platforms (AWS/Azure/GCP).

Project Overview

UADAMAGE is hiring a Data Engineer to join a cross-functional team working on a national-scale Big Data platform for Ukraine. The project focuses on building a geospatial platform for multifactor analysis of territories to support demining and recovery planning. The system integrates remote sensing, environmental and socio-economic indicators, and includes both Big Data infrastructure and AI/ML modules. The platform is under active development with support from governance and international partners.

Role Summary

Design and maintain scalable and sustainable data pipelines within the platform to ensure timely, accurate, and consistent integration of multi-source data. Develop and support workflows for data ingestion, cleansing, normalization, and transformation from internal and external sources (e.g. government systems, commercial registers, API feeds). Ensure compatibility of all data pipelines with downstream use in GIS systems, land analysis models, dashboards and reports. Execute basic data analysis. Collaborate with product, analytics, and engineering teams to maintain data quality and feasibility for decision-support tools. Support data architecture evolution, implement data processing best practices, and contribute to technical documentation.

Qualifications required

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or related fields.
• 3+ years of experience in data engineering or data platform development.
• Proven experience in building and maintaining data pipelines (ETL/ELT), performing data cleansing, normalization, and transformation of structured and unstructured datasets.
• Hands-on experience with technologies such as Python, PySpark, Scala, SQL, Pandas, NumPy and tools including Apache Spark, Hadoop, and Amazon EMR.
• Experience working with AWS-based platforms, including S3 and cloud-native data processing services.
• Practical knowledge of streaming frameworks and distributed data processing patterns, such as MapReduce and real-time cloud pipelines.
• Experience integrating data from diverse sources (relational databases, APIs, satellite/geospatial data, file systems) for use in analytical models and data management platforms.
• Understanding of data integration strategies, API/data services, and long-term system maintainability.
• Familiarity with best practices in data documentation, versioning, metadata tracking, and validation.
• Familiarity with geospatial data formats, geo coordinate systems, and spatial preprocessing is an asset.
• Experience working in complex or multi-stakeholder environments is an asset.

• Proficiency in Ukrainian and English is required.

   

We offer

• Competitive salary
• Motivated and engaged team
• Open communication and active knowledge sharing
• Direct support from project leadership
• Clear specifications and well-defined requirements
• Opportunity to contribute to a high-impact, nationally relevant initiative

About the client
Haptiq is an AI-native enterprise solutions company with purpose-built technology for public & private companies, governments, institutions, asset managers, and family offices

With headquarters in New York City and four global offices, Haptiq is supported by more than 300 engineers and delivery professionals across the globe. By centralizing and unifying data, automating workflows, and surfacing predictive insights, Haptiq enables organizations to scale operational excellence and generate alpha across complex enterprise environments

The Opportunity
This position is for a Cloud Data Engineer with a background in Python, DBT, SQL, and data warehousing for enterprise-level systems
 

Responsibilities and Duties
— Design, develop, and deploy Python scripts and ETL processes with Prefect and Airflow to prepare data for analysis
— Model dimensional and denormalized schemas for optimal performance reporting and discovery
— Design AI-friendly DB schemas and ontologies
— Architect cloud ops solutions for data topologies
— Transform and migrate data with Python, DBT, and Pandas
— Work with event-based/streaming technologies for real-time ETL
— Ingest and transform structured, semi-structured, and unstructured data
— Optimize ETL jobs for performance and scalability to handle big data workloads
— Monitor and troubleshoot ETL jobs to identify and resolve issues or bottlenecks
— Implement best practices for data management, security, and governance with Prefect, DBT, and Pandas
— Write SQL queries and program stored procedures and reverse engineer existing data pipelines
— Perform code reviews to ensure fit to requirements, optimal execution patterns, and adherence to established standards
— Assist with automated release management and CI/CD processes
— Validate and cleanse data and handle error conditions gracefully
 

Requirements
— 3+ years of Python development experience, including Pandas
— 5+ years writing complex SQL queries with RDBMSes
— 5+ years of Experience with developing and deploying ETL pipelines using Airflow, Prefect, or similar tools
— Experience with cloud-based data warehouses in environments such as RDS, Redshift, or Snowflake
— Experience with data warehouse design: OLTP, OLAP, Dimensions, and Facts
— Experience with cloud-based data architectures, messaging, and analytics
— Bachelor’s degree in Computer Science or equivalent — preferred
 

Pluses — Experience with
— Docker
— Kubernetes
— CI/CD automation
— AWS lambdas/step functions
— Data partitioning
— Databricks
— Pyspark
— Cloud certifications
 

Why Join Us?
We value creative problem solvers who learn fast, work well in an open and diverse environment, and enjoy pushing the bar for success ever higher. We do work hard, but we also choose to have fun while doing it