Company Overview

The organisation relies on dependable data infrastructure to support business reporting, operational analysis, product development, and evidence-based decision-making. Its technology teams work with information from multiple systems and sources, transforming raw data into reliable, structured datasets that can be used by analysts, business stakeholders, and digital applications.

The organisation values data quality, scalable engineering practices, secure information management, and continuous improvement. It seeks a Data Engineer who can develop robust data pipelines, maintain analytical data environments, and improve the consistency and accessibility of information across the business.

The successful candidate will work collaboratively with analysts, software engineers, product teams, and other stakeholders to ensure that data is collected, processed, validated, and delivered according to defined business requirements. The organisation encourages technical ownership, clear documentation, and practical solutions that support long-term maintainability.

Role Summary

The Remote Data Engineer will design, build, maintain, and optimise data pipelines that transform information from operational systems into reliable datasets for reporting, analytics, and downstream applications. The role involves extracting data from databases and APIs, implementing transformation logic, managing scheduled workflows, and maintaining data models that support consistent analysis.

The successful candidate will use SQL, Python, and appropriate data engineering technologies to develop repeatable processing workflows and improve data availability. Responsibilities will also include monitoring pipeline performance, investigating failed jobs, implementing data validation checks, and helping maintain reliable cloud-based or warehouse environments.

Working remotely within Estonia, the Data Engineer will collaborate with technical and non-technical colleagues to understand data requirements, resolve data-quality problems, and deliver solutions that balance performance, cost, security, and maintainability.

Key Responsibilities

• Design, develop, and maintain automated data pipelines that collect, transform, validate, and deliver information from operational databases, APIs, files, and other approved sources.
• Build data ingestion and transformation workflows using Python, SQL, and suitable data engineering frameworks.
• Develop and maintain extract, transform, and load (ETL) or extract, load, and transform (ELT) processes for analytical reporting and business intelligence.
• Design and maintain data models that organise information into consistent, well-documented structures for downstream analysis.
• Work with data warehouses and cloud-based data platforms to support efficient storage, processing, and retrieval of business information.
• Write and optimise SQL queries used in data transformations, analytical datasets, quality checks, and reporting workflows.
• Implement data validation rules to detect missing values, duplicate records, inconsistent formats, unexpected changes, and other quality issues.
• Monitor scheduled pipelines and data workflows, investigating failed jobs, processing delays, and discrepancies in output datasets.
• Establish appropriate logging, alerting, retry mechanisms, and failure-handling procedures to improve pipeline reliability.
• Collaborate with data analysts and business stakeholders to understand reporting requirements and translate them into reliable data structures.
• Work with software engineers to integrate application data sources and coordinate changes to APIs, database schemas, and data contracts.
• Maintain data lineage and documentation describing source systems, transformation rules, dependencies, and downstream data consumers.
• Apply appropriate access controls and data protection practices when processing personal, commercial, or otherwise sensitive information.
• Improve pipeline efficiency by reviewing execution times, data volumes, query performance, processing methods, and unnecessary transformations.
• Support the development and maintenance of data warehouse tables, reusable transformation models, and shared data definitions.
• Participate in data architecture discussions and recommend practical approaches to improve scalability, consistency, and maintainability.
• Assist with the investigation of data discrepancies between operational systems, warehouse records, dashboards, and financial or business reports.
• Use version control, code reviews, automated tests, and deployment workflows to maintain the quality and traceability of data engineering changes.
• Support the introduction of new data sources by assessing data structures, validating source quality, and documenting integration requirements.
• Collaborate with infrastructure or DevOps colleagues on cloud environments, job scheduling, credentials management, and deployment configuration.
• Identify opportunities to reduce manual data preparation and improve the automation of recurring analytical processes.
• Contribute to technical documentation, data dictionaries, operational runbooks, and procedures for troubleshooting common pipeline issues.

Requirements and Qualifications

• Professional experience in data engineering, analytics engineering, ETL development, or a closely related technical role.
• Strong SQL skills, including joins, aggregations, common table expressions, and query optimisation.
• Practical programming experience with Python or another suitable language for data processing and automation.
• Understanding of data modelling, relational databases, data warehouses, and analytical data structures.
• Experience developing or maintaining ETL/ELT pipelines and integrating information from multiple sources.
• Familiarity with at least one cloud environment or data platform, such as AWS, Microsoft Azure, Google Cloud, Snowflake, or BigQuery.
• Experience with workflow orchestration tools or data transformation frameworks is advantageous.
• Understanding of data quality controls, data validation, data lineage, and reliable processing practices.
• Familiarity with Git, version control, automated testing, and collaborative development workflows.
• Ability to investigate data discrepancies and identify the underlying causes of pipeline or transformation failures.
• Knowledge of APIs, JSON, scheduled jobs, and database connectivity is beneficial.
• Strong analytical thinking, attention to detail, and a structured approach to solving technical problems.
• Ability to document data processes clearly and communicate technical findings to non-technical stakeholders.
• Good time management and the ability to work independently while coordinating with a distributed team.
• A degree in computer science, data engineering, information systems, statistics, or a related field is advantageous; equivalent practical experience is also valued.
• Professional proficiency in English is required; knowledge of Estonian is an advantage.

Benefits

• Fully remote working arrangement within Estonia.
• Full-time, permanent employment in a data engineering role focused on reliable analytics infrastructure.
• Opportunities to work with modern cloud data platforms, data warehouses, and automated processing workflows.
• Collaboration with data analysts, software engineers, and business stakeholders across different functions.
• Opportunities to influence data architecture, quality controls, pipeline reliability, and engineering standards.
• Professional development through technical collaboration, code reviews, and exposure to evolving data technologies.
• Opportunities to improve data accessibility and reduce manual reporting effort across the organisation.
• A working environment that values engineering quality, secure data handling, ownership, and continuous learning.

Application Instructions

Applicants should submit an up-to-date CV outlining their data engineering experience, programming skills, and relevant projects. Highlight your experience with SQL, Python, data pipelines, cloud platforms, data warehouses, and data quality processes.

Candidates may include a portfolio, Git repository, or project descriptions demonstrating data ingestion, transformation, orchestration, or warehouse development. Do not include confidential datasets, access credentials, proprietary code, or sensitive information belonging to previous employers.

Applications will be assessed on technical capability, data engineering fundamentals, problem-solving skills, attention to data quality, and readiness to work effectively in a remote environment. Please apply through the official application channel associated with the vacancy and provide only information relevant to the recruitment process.

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