Data Engineer

The Data Engineer is responsible for designing, building, and maintaining the data infrastructure that powers analytics, reporting, and data‑driven decision‑making across the organization. This role ensures that data is collected, processed, and delivered efficiently, reliably, and securely, enabling teams to leverage high‑quality datasets for business insights and product innovation. Key Responsibilities Data Architecture & Pipeline Development Design, build, and maintain scalable data pipelines and ETL/ELT processes. Develop and optimize data architectures to support analytics, reporting, and machine learning workloads. Ensure data models and structures are efficient, well documented, and aligned with business needs. Data Management & Quality Implement data quality checks, validation rules, and monitoring processes to ensure accuracy and reliability. Develop and maintain data dictionaries, metadata documentation, and data governance standards. Ensure compliance with data privacy, security, and regulatory requirements. Operational Management & Optimization Monitor pipeline performance, troubleshoot issues, and implement performance improvements. Optimize data storage solutions to balance cost, speed, and scalability. Automate recurring data engineering tasks to improve operational efficiency. Cross Functional Collaboration Partner with data analysts, data scientists, product teams, and business stakeholders to understand data requirements. Translate business needs into technical specifications for data ingestion, transformation, and delivery. Collaborate with engineering teams to integrate data workflows into products and services. Data Analysis & Reporting Support Enable self service analytics by preparing curated datasets and developing reusable data assets. Support development of dashboards and reports by ensuring data availability and structure. Provide technical guidance on data best practices to non technical stakeholders. Requirements Experience: 2–5 years of experience in data engineering, data warehousing, or backend engineering with a strong data focus. Experience designing and maintaining production‑grade ETL/ELT pipelines. Experience working with cloud‑based data platforms. Level of Education: Bachelor’s Degree Field: Computer Science, Information Systems, Engineering, Mathematics, or a related field. Language: Fluent English Job Specific Skills: Strong understanding of data modeling, ETL/ELT best practices, and database design Ability to work with large‑scale datasets and distributed systems Strong analytical and problem‑solving skills Ability to translate business requirements into data engineering solutions Solid understanding of data governance, data quality frameworks, and privacy requirements Effective communication and cross‑functional collaboration skills IT Skills: Proficiency with SQL and relational databases (e.g., PostgreSQL, MySQL, SQL Server) Experience with big data or cloud data technologies (e.g., AWS Redshift, Google BigQuery, Azure Synapse, Databricks, Snowflake) Hands‑on experience with data pipeline tools (Airflow, dbt, Glue, Dataflow, Kafka, Spark) Proficiency in programming languages such as Python or Scala Familiarity with API integrations and data ingestion frameworks Experience with version control and CI/CD workflows (Git, GitHub, GitLab) Knowledge of containerization or orchestration (Docker, Kubernetes) is a plus Experience with data visualization tools (Power BI, Tableau, Looker) is a plus Benefits The company is committed to diversity and inclusion and is an equal opportunity employer. Apply now.