Data Engineer

Company Description Hash Technologies is a leading consulting firm that partners with organizations to address complex challenges across technology, engineering, healthcare, construction, and other sectors. The team focuses on innovative, results-driven strategies that support both current operational needs and long-term business growth. Hash designs advanced technology solutions and strategic business plans tailored to each client’s goals, ensuring competitiveness in a fast-changing market. By customizing its approach, the firm helps clients embrace digital transformation, optimize processes, and build scalable, future-ready solutions. Regardless of project size or complexity, Hash Technologies aims to turn challenges into opportunities and deliver measurable outcomes.
Data Engineer Job Description tailored precisely to the time allocation and responsibilities you provided.
Job Title: Data Engineer
Job Summary
We are seeking a skilled and detail-oriented Data Engineer to design, build, and scale our core data infrastructure. In this role, you will be responsible for creating robust data pipelines, integrating disparate data sources, optimizing database performance, and ensuring the highest standards of data quality. You will collaborate closely with engineering, analytics, and business teams to transform raw data into high-quality, production-ready datasets that drive critical business decisions.
Core Responsibilities & Time Allocation
Data Pipeline Design and Development (25%)
Design, develop, and maintain scalable data pipelines and ETL/ELT processes to collect, transform, and load data from various sources.
Build reusable data processing components to streamline workflows and reduce engineering overhead.
Ensure reliable and efficient data movement across interconnected internal and external systems.
Data Integration, Transformation, and Processing (25%)
Develop and maintain data integration workflows to ingest both structured and unstructured data from databases, APIs, applications, and third-party sources.
Execute rigorous data cleansing, transformation, validation, and enrichment protocols.
Prepare high-quality, optimized datasets tailored for analytics, reporting, and broad business operations.
Cloud Data Platforms and Data Infrastructure (15%)
Configure and maintain data infrastructure utilizing modern cloud-based data platforms and services.
Implement scalable storage and high-throughput processing solutions capable of handling expanding data volumes.
Support and align data workflows smoothly across development, testing, and production environments.
Database Development and Optimization (10%)
Develop and optimize complex SQL queries, stored procedures, data models, and database processes.
Monitor database performance, proactively identify bottlenecks, and resolve data-related technical issues.
Implement architectural improvements to ensure rapid, highly efficient data retrieval.
Data Quality, Monitoring, and Troubleshooting (15%)
Monitor active data pipelines and processing jobs to immediately flag failures, inconsistencies, or performance lags.
Perform deep root-cause analysis to isolate and permanently resolve underlying data quality problems.
Deploy strict validation mechanisms and alerting systems to safeguard data accuracy, completeness, and reliability.
Documentation, Reporting, and Continuous Improvement (10%)
Maintain thorough technical documentation outlining data pipelines, data models, schemas, workflows, and configurations.
Prepare operational reports detailing system health, processing metrics, and data throughput.
Partner with cross-functional teams (engineering, analytics, business) to continuously iterate and improve data architecture, reliability, and speed.
Required Skills & Qualifications
Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, or a highly related technical field.
Experience: 3–5+ years of verified hands-on experience in a Data Engineering or Big Data Developer role.
Database Mastery: Advanced proficiency in SQL, schema design, dimensional data modeling, and performance tuning.
Programming Languages: Strong coding skills in Python, Java, or Scala for writing data extraction and transformation scripts.
Cloud & Tools: Proven experience working with cloud data platforms (e.g., AWS, Snowflake, Azure, or Google Cloud) and modern orchestration/ETL tools.
Big Data Frameworks: Familiarity with distributed compute frameworks like Spark or Hadoop is a major plus.
Soft Skills: Exceptional problem-solving traits, a strong eye for detail, and the ability to articulate complex technical data architectures to non-technical business partners.

Work arrangement
Yes