Torchora
Back to Home

Key Responsibilities and Required Skills for Associate Data Engineer

💰 $65,000 - $95,000

Data EngineeringITAnalyticsCloud

🎯 Role Definition

The Associate Data Engineer is an early-career engineering professional responsible for designing, building, and maintaining reliable, scalable data pipelines and data infrastructure that enable analytics, reporting, and data products. This role focuses on ETL/ELT development, data modeling, data quality, and close collaboration with analysts, data scientists, and software engineers. The ideal candidate demonstrates strong SQL and Python skills, a working knowledge of cloud data platforms (AWS, GCP, or Azure), and a pragmatic approach to operationalizing data for business insights.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning into engineering-focused work with SQL and scripting experience.
  • Junior or Graduate Data Engineer who has completed an internship or bootcamp.
  • Software Engineer or Backend Developer with an interest in analytics and cloud data systems.

Advancement To:

  • Data Engineer
  • Senior Data Engineer
  • Analytics Engineering Lead / Data Platform Engineer

Lateral Moves:

  • Business Intelligence (BI) Developer
  • Machine Learning Engineer
  • Analytics or Reporting Specialist

Core Responsibilities

Primary Functions

  • Design, implement, and maintain robust ETL/ELT data pipelines using Python, SQL, and orchestration tools (e.g., Airflow, Prefect), ensuring timely ingestion and transformation of structured and semi-structured data from multiple sources.
  • Build and maintain data models and schemas in cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and ensure they are optimized for query performance and downstream analytics.
  • Author, review, and optimize complex SQL queries for data extraction, reporting, and analytics while following best practices for performance and maintainability.
  • Develop reusable data ingestion patterns and libraries to standardize onboarding of new data sources, including APIs, event streams (Kafka/Kinesis), and batch file systems.
  • Implement data validation, profiling, and automated testing (unit and integration tests) for pipelines to detect data anomalies and prevent regressions in production.
  • Collaborate with data analysts and data scientists to translate business requirements into technical specifications, deliverables, and production-grade data sets.
  • Monitor pipeline health and reliability using observability and monitoring tools (e.g., Datadog, Prometheus, CloudWatch) and implement alerting and incident response playbooks.
  • Apply data governance practices including data lineage, schema versioning, and documentation to ensure data discoverability, privacy, and compliance with policies.
  • Optimize and refactor existing ETL jobs and SQL logic to reduce runtime, cost, and resource usage while preserving accuracy and reliability.
  • Implement CI/CD workflows for data pipeline deployment using Git, GitHub Actions, GitLab CI, or similar tooling to enable safe, repeatable releases to production.
  • Assist in the migration and modernization of legacy on-premise data processes to cloud-native architectures and managed services.
  • Participate in code reviews and share knowledge with peers to improve code quality and team-wide engineering standards.
  • Create and maintain clear documentation, runbooks, and onboarding guides for datasets, pipelines, and platform components to support team scalability.
  • Work with data security and compliance teams to implement data access controls, role-based permissions, and encryption where required.
  • Contribute to the design and implementation of data catalogs and metadata platforms to support data discovery and governance initiatives.
  • Support development of near-real-time data pipelines using streaming frameworks or managed streaming services, and ensure end-to-end throughput and latency requirements are met.
  • Profile, clean, and enrich raw data to produce high-quality, analysis-ready datasets; implement transformations that capture business logic and KPIs.
  • Collaborate with platform and infrastructure teams to provision, configure, and tune cloud resources (compute, storage, and networking) to meet pipeline SLAs and budget constraints.
  • Troubleshoot production incidents, perform root cause analysis, and drive permanent fixes to prevent reoccurrence while communicating status to stakeholders.
  • Create dashboards and lightweight metrics to measure data pipeline performance, data quality metrics, and business-impacting KPIs.
  • Engage in sprint planning and agile ceremonies; estimate tasks, deliver incremental value, and maintain an accountable cadence of work.
  • Stay current on modern data engineering trends, open-source tools, and cloud services; propose practical improvements and proof-of-concepts to enhance the data platform.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to help business teams validate hypotheses and make data-driven decisions.
  • Contribute to the organization's data strategy and roadmap by identifying technical debt, opportunities for automation, and cost-saving improvements.
  • Collaborate with business units to translate data needs into engineering requirements, prioritizing based on impact and effort.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Mentor interns or junior engineers, providing code-level feedback and practical guidance on data engineering best practices.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: complex joins, window functions, CTEs, query optimization and performance tuning.
  • Python (or Scala) for data engineering: ETL scripting, data transformations, libraries like pandas, pyarrow.
  • Knowledge of cloud data warehouses and lakehouses: Snowflake, BigQuery, Redshift, Databricks or similar.
  • Experience with orchestration tools: Apache Airflow, Prefect, Luigi, or equivalent scheduling frameworks.
  • Familiarity with streaming technologies: Kafka, Kinesis, Pub/Sub, or event-driven architectures for realtime data flows.
  • Data modeling fundamentals: star/snowflake schema design, dimensional modeling, normalization/denormalization trade-offs.
  • ETL/ELT best practices and tools: dbt, Talend, Matillion, or custom in-house frameworks.
  • Version control and CI/CD: Git, branching strategies, automated testing and deployment pipelines.
  • Monitoring and observability: logging, metrics, alerting tools and techniques for production pipelines.
  • Basics of cloud infrastructure and services: AWS (S3, EMR, Lambda), GCP (Cloud Storage, Dataflow), or Azure equivalents.
  • Data quality and testing: unit/integration tests for pipelines, data validation frameworks, and anomaly detection.
  • Knowledge of data governance, metadata management, and compliance considerations (PII handling, GDPR basics).
  • Familiarity with containerization and orchestration (Docker, Kubernetes) is a plus.
  • Experience with performance tuning and cost optimization in cloud environments.

Soft Skills

  • Strong analytical thinking and problem-solving orientation; ability to diagnose issues from logs and metrics quickly.
  • Clear written and verbal communication for documenting processes and explaining technical concepts to non-technical stakeholders.
  • Collaborative mindset and experience working in cross-functional teams with product managers, analysts, and engineers.
  • Detail-oriented with a commitment to data accuracy, repeatability, and operational excellence.
  • Adaptability and eagerness to learn new tools, frameworks, and cloud technologies in a fast-evolving data landscape.
  • Time management and ability to prioritize tasks in an agile environment while balancing short-term fixes and long-term improvements.
  • Proactive ownership and accountability for delivering reliable data products and resolving incidents.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, Statistics, Mathematics, or a closely related technical field.
  • Equivalent practical experience through coding bootcamps or relevant internships may be accepted.

Preferred Education:

  • Master's degree in Data Science, Computer Science, Analytics, or related field is a plus.
  • Certifications in cloud platforms (AWS/GCP/Azure) or data engineering tools (dbt, Snowflake) are beneficial.

Relevant Fields of Study:

  • Computer Science
  • Data Science / Analytics
  • Information Systems
  • Mathematics / Statistics
  • Software Engineering

Experience Requirements

Typical Experience Range: 1–3 years of hands-on experience in data engineering, analytics engineering, or a related software role; internships and project experience count.

Preferred: 2+ years of production experience building ETL/ELT pipelines, working with cloud data warehouses, and using SQL and Python daily. Experience contributing to an operational data platform, implementing CI/CD for data artifacts, and working within an Agile team is highly desirable.

Explore More Careers