Key Responsibilities and Required Skills for Data Engineering Analyst

🎯 Role Definition

The Data Engineering Analyst is a hybrid technical and analytical role responsible for designing, building, and maintaining reliable, scalable data pipelines and data products that power analytics, BI, and machine learning. This role partners closely with data scientists, analysts, product managers, and engineering teams to translate business requirements into production-grade ETL/ELT processes, curated data models, and documented datasets. The ideal candidate combines strong SQL and programming skills with cloud data platform experience, a deep understanding of data modeling and warehousing, and a disciplined approach to data quality, observability, and documentation.

Core SEO & LLM keywords: Data Engineering Analyst, ETL/ELT, data pipelines, cloud data warehouse, Snowflake, BigQuery, Redshift, Spark, Airflow, dbt, SQL, Python, data modeling, data governance, data quality, streaming, Kafka, data catalog.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst transitioning to engineering-focused responsibilities and automation.
Business Intelligence (BI) Analyst or BI Developer moving into pipeline ownership.
Junior Software Engineer interested in data infrastructure and analytics pipelines.

Advancement To:

Senior Data Engineer / Lead Data Engineer
Analytics Engineering Manager or Data Engineering Manager
Data Architect or Principal Data Engineer
Machine Learning Engineer or ML Platform Engineer (with MLOps focus)

Lateral Moves:

Analytics Engineer (dbt-focused)
BI Engineer / Dashboarding Specialist
Data Governance Analyst or Data Steward
Platform Reliability Engineer (data platform focus)

Core Responsibilities

Primary Functions

Design, build, and maintain scalable, fault-tolerant ETL/ELT data pipelines using modern tools (SQL, Python, Spark, dbt, Airflow) to ingest, transform, and load structured and semi-structured data from internal systems and external sources into cloud data warehouses (e.g., Snowflake, BigQuery, Redshift).
Implement and manage batch and streaming ingestion patterns (Kafka, Pub/Sub, Kinesis) to support near-real-time analytics and event-driven data products while ensuring exactly-once or idempotent processing semantics where required.
Develop and maintain robust data models and dimensional schemas (star/snowflake) and curate analytics marts that enable fast, consistent reporting and BI across the organization.
Author production-quality SQL and Python code for complex data transformations, aggregations, joins, window functions, and performance-optimized queries for large datasets.
Create and enforce data quality checks, monitoring, and alerting (profiling, validation, reconciliation) to detect and remediate pipeline issues early and to maintain SLA-driven data freshness and accuracy.
Design and implement data partitioning, clustering, and distribution strategies to optimize query performance and control cloud costs in data warehouses and data lakes.
Build and maintain orchestration workflows (Airflow, Prefect, Dagster) with clear dependency management, retries, backfills, and SLA policies; maintain runbooks and incident procedures.
Implement CI/CD pipelines for data code, infrastructure-as-code (Terraform/CloudFormation), and pipeline deployments; use Git-based workflows and pull-request review processes to ensure code quality and traceability.
Collaborate with data scientists and analysts to productionize feature pipelines and reusable datasets, ensuring reproducibility, lineage, and appropriate data access controls are in place.
Define and maintain metadata, dataset catalogs, and data lineage (using tools like Data Catalog, Amundsen, or Monte Carlo) so analysts and stakeholders can discover, trust, and understand data assets.
Optimize and tune Spark jobs, SQL queries, and cloud resources to reduce latency and cost; profile workloads and recommend instance sizes, cluster autoscaling, and resource pools.
Build and maintain comprehensive documentation: schema definitions, transformation logic, data dictionaries, access patterns, and operational runbooks to facilitate cross-team adoption and knowledge transfer.
Implement and enforce data governance, security, and privacy practices (role-based access control, PII masking, encryption at rest/in transit, GDPR/CCPA considerations) in collaboration with security and compliance teams.
Troubleshoot production incidents, perform root-cause analysis for data outages or corruption, and drive remediation with permanent fixes, postmortems, and preventive measures.
Develop reusable libraries, templates, and abstractions for common data tasks (ingestion connectors, transformation templates, testing utilities) to accelerate delivery and standardize best practices.
Partner with product managers, stakeholders, and business analysts to translate ambiguous business questions into clear data requirements, acceptance criteria, and prioritized backlog items.
Participate in architecture reviews and technology selection; evaluate and pilot new data platform components (lakehouse, streaming frameworks, managed services) against reliability, scalability, and cost criteria.
Implement dataset versioning, schema evolution strategies, and backward-compatible transformations to minimize downstream breakage when source schemas change.
Monitor and report on key metrics for data platform health: pipeline success rate, data freshness SLA compliance, query latency, and storage/cost trends — and propose optimizations to improve them.
Mentor junior data engineers and analysts on best practices for SQL, data modeling, testing, observability, and production deployment patterns to raise overall team capability.
Coordinate cross-functional releases and data migrations, ensuring minimal disruption to reporting and dependent downstream consumers.
Build sandbox environments and self-service data delivery patterns to empower analysts while maintaining governance and controls for production datasets.
Implement automated unit, integration, and regression testing for data transformations using frameworks like Great Expectations, dbt tests, or custom test suites to ensure correctness of outputs.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL: complex joins, window functions, CTEs, query optimization, and explain-plan analysis for large datasets.
Programming (Python preferred): scripting for ETL tasks, data manipulation (pandas, pyarrow), and production-grade application code.
Big Data processing: Apache Spark (PySpark/Scala) or equivalent distributed processing frameworks for large-scale transformations.
Cloud data platforms: hands-on experience with at least one major provider — Snowflake, BigQuery, Redshift, Databricks, or Azure Synapse.
Workflow orchestration: Airflow, Prefect, Dagster, or equivalent scheduling and DAG management tools.
ELT frameworks and analytics engineering: dbt (transformations-as-code), modular SQL development, and test-driven data modeling.
Streaming & messaging: Kafka, Confluent, Google Pub/Sub, or AWS Kinesis for event-driven data ingestion and processing.
Data modeling and warehousing: dimensional modeling, normalization/denormalization trade-offs, and best practices for building analytics-ready schemas.
Data quality & testing: experience with Great Expectations, dbt tests, or custom validation frameworks and automated data tests.
Infrastructure-as-code & CI/CD: Terraform, CloudFormation, GitHub Actions, GitLab CI, or Jenkins for reproducible deployments and platform automation.
Observability & monitoring: Prometheus, Datadog, New Relic, Monte Carlo, or built-in cloud metrics + logging for pipeline health and alerting.
Version control & collaboration: Git workflows, code reviews, branching strategies, and pull-request driven development.
Security & governance: RBAC, IAM, encryption, PII handling, GDPR/CCPA knowledge, and implementing least-privilege access.
SQL-based BI tools (desirable): Looker, Tableau, Power BI, or equivalent for translating engineered data into consumable analytics.
Containerization & orchestration (optional but valuable): Docker, Kubernetes for scalable microservices or data-processing workloads.

Soft Skills

Strong problem-solving and analytical thinking with an attention to detail and data accuracy.
Clear communicator who can translate technical trade-offs and constraints into business-impact language for stakeholders.
Proactive ownership mindset with a commitment to operational excellence, SLAs, and post-incident follow-through.
Collaboration and cross-functional teamwork — able to work with product, analytics, engineering, and compliance teams.
Prioritization and time management in fast-paced environments with competing business needs.
Mentoring and coaching capability to uplift junior teammates and share best practices.
Curiosity and continuous learning mindset: staying current with data platform innovations and industry best practices.
Customer-focused attitude: designing solutions that make data easy, reliable, and discoverable for end users.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Systems, Mathematics, Statistics, or a related technical field.

Preferred Education:

Master’s degree in Computer Science, Data Science, Engineering, or Business Analytics (optional but preferred for advanced analytics roles).
Certifications (helpful): AWS Certified Data Analytics, Google Professional Data Engineer, SnowPro Core, Databricks Certified Data Engineer, dbt Fundamentals.

Relevant Fields of Study:

Computer Science / Software Engineering
Data Science / Statistics / Applied Mathematics
Information Systems / Business Analytics
Electrical Engineering or related quantitative disciplines

Experience Requirements

Typical Experience Range:

2–5 years of hands-on professional experience in data engineering, analytics engineering, or ETL/BI development. (Mid-level roles typically expect 3+ years.)

Preferred:

3–7 years of experience building and operating production data pipelines and cloud data warehouses.
Demonstrated experience with SQL-heavy transformation work, Python or Spark programming, orchestration tools (Airflow), and at least one major cloud data platform (Snowflake, BigQuery, Redshift, Azure Synapse).
Proven track record of delivering cross-functional data products, implementing data quality/testing frameworks, and contributing to data governance and security initiatives.
Exposure to streaming data and real-time analytics is highly desirable.