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Key Responsibilities and Required Skills for Data Solutions Engineer

💰 $110,000 - $160,000

Data EngineeringAnalyticsCloudSoftware Engineering

🎯 Role Definition

This role requires a hands-on Data Solutions Engineer to design, build, and operate robust, scalable data platforms and end-to-end data solutions that enable analytics, reporting, ML, and product use cases. The Data Solutions Engineer partners with product managers, data scientists, analysts, and platform teams to translate business requirements into production-grade ETL/ELT pipelines, efficient data models, and documented data contracts. This role emphasizes cloud-native engineering, observability, data governance, cost optimization, and repeatable delivery patterns using modern tooling (SQL, Python, dbt, Airflow, Snowflake/BigQuery/Redshift, Kafka, Terraform).

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning into engineering-focused delivery
  • Business Intelligence Engineer or Analytics Engineer with strong SQL/ETL background
  • Software Engineer with cloud or backend experience interested in data systems

Advancement To:

  • Senior Data Solutions Engineer / Lead Data Engineer
  • Data Engineering Manager or Head of Data Platforms
  • Solutions Architect, Data Architect, or Director of Analytics Engineering

Lateral Moves:

  • Data Architect
  • Machine Learning Engineer / MLOps Engineer
  • Platform Engineer for data infrastructure

Core Responsibilities

Primary Functions

  • Design, build, and maintain scalable ETL/ELT pipelines and data ingestion systems that reliably move high-volume data from transactional, event, and third‑party sources into the cloud data warehouse or data lake, using tools such as dbt, Airflow, Spark, AWS Glue, or equivalent orchestration frameworks.
  • Architect and implement dimensional and normalized data models, star schemas, and semantic layers to support BI dashboards, ad-hoc analysis, and machine learning feature engineering while ensuring query performance and low cost.
  • Develop, test, and optimize SQL, Python, and Spark code to transform raw data into clean, well-documented datasets and materialized views, applying best practices for query optimization, partitioning, and clustering.
  • Build and operate cloud-native data platforms on AWS, GCP, or Azure — provisioning and tuning services such as Snowflake, BigQuery, Redshift, Synapse, S3/Cloud Storage, and Delta Lake to meet SLAs for latency, throughput, and cost-efficiency.
  • Implement CI/CD pipelines, infrastructure-as-code (Terraform, CloudFormation), and containerization (Docker, Kubernetes) for repeatable, auditable deployment of data pipelines, connectors, and platform components.
  • Create and enforce data quality frameworks and automated testing (unit, integration, regression) using tools like Great Expectations, dbt tests, and custom validation to monitor freshness, accuracy, and completeness.
  • Own the end-to-end operational reliability of production data workflows: implement monitoring, alerting, runbooks, and on-call rotations; perform root cause analysis and postmortems for incidents.
  • Integrate streaming and real-time data sources using Kafka, Kinesis, Pub/Sub, or managed streaming services, designing low-latency processing pipelines and ensuring exactly-once or at-least-once guarantees as required.
  • Collaborate with product managers, analytics, and data science teams to translate business requirements into data contracts, SLAs, schemas, and prioritized delivery backlogs.
  • Design and implement data access controls, encryption, masking, and role-based access policies to meet security and compliance requirements (GDPR, CCPA, HIPAA where applicable).
  • Maintain data catalog, metadata management, and lineage (e.g., Amundsen, Data Catalog, Collibra) to improve discoverability, governance, and compliance across datasets.
  • Lead performance tuning efforts across the stack — SQL query optimization, cluster sizing, caching, materialized views, and partition strategies — to reduce latency and cloud spend.
  • Implement schema evolution strategies and backward-compatible migrations for tables, views, and streaming topics to avoid downstream breakages.
  • Build reusable data platform components, templates, and reference implementations to accelerate new data product development and reduce engineering debt.
  • Author clear technical documentation, data dictionaries, onboarding guides, and runbooks that enable cross-functional teams to discover and reliably use data assets.
  • Conduct code reviews, mentor junior engineers, and establish engineering standards and best practices for testing, observability, and secure deployments.
  • Design APIs and data services (REST/GraphQL, gRPC) or data extracts to support application integration and product feature delivery.
  • Lead cost-optimization initiatives across storage, compute and query patterns, applying autoscaling, compute credit management, and lifecycle policies to minimize run costs.
  • Prototype new ingestion and processing patterns (serverless, streaming, micro-batch) and evaluate third-party SaaS/managed services for fit and total cost of ownership.
  • Coordinate cross-functional releases and data migrations with product, backend, and platform engineering teams to minimize user impact and ensure backward compatibility.
  • Establish and track KPIs for data platform health, data quality, latency, and business impact; present findings and roadmaps to stakeholders and leadership.
  • Ensure observability across data pipelines by integrating logging, metrics, tracing, and dashboards (Prometheus, Grafana, CloudWatch, Stackdriver) and tuning alerts to actionable thresholds.

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.
  • Facilitate technical workshops and training for analysts, data scientists, and stakeholders to promote self-service analytics.
  • Evaluate and onboard data integration partners, ETL tools, and vendor solutions; negotiate SLOs and manage vendor relationships.
  • Assist with regulatory audits and provide documentation for data lineage, retention, and access controls as requested by security and compliance teams.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: complex joins, window functions, CTEs, query profiling, and optimization across Snowflake, BigQuery, Redshift, or similar.
  • Python programming for data engineering: ETL scripting, data validation, orchestration, and building reusable libraries.
  • Data orchestration and transformation frameworks: dbt, Apache Airflow, Prefect, Luigi, or equivalent.
  • Cloud data platform experience: Snowflake, BigQuery, Redshift, Azure Synapse, Databricks or equivalent.
  • Big data processing: Spark (PySpark/Scala), Hadoop ecosystem, or managed serverless data processing.
  • Real-time/streaming technologies: Kafka, Kinesis, Pub/Sub, stream processing (Kafka Streams, Flink, Spark Structured Streaming).
  • Infrastructure-as-code and CI/CD: Terraform, CloudFormation, GitHub Actions, Jenkins, or CircleCI.
  • Containerization and orchestration: Docker and Kubernetes (EKS/GKE/AKS) basics for containerized data services.
  • Data modeling and dimensional design: star schema, slowly changing dimensions (SCD), normal forms, and canonical schema patterns.
  • Data quality and testing tools: Great Expectations, dbt tests, unit/integration testing frameworks.
  • Observability and monitoring: Prometheus, Grafana, CloudWatch, Stackdriver, ELK/EFK, Sentry.
  • Metadata and data catalog tools: Amundsen, Data Catalog, Collibra, Alation; knowledge of lineage and governance concepts.
  • Security & compliance fundamentals: IAM, encryption at rest/in transit, RBAC, tokenization, masking, GDPR/CCPA considerations.
  • API design and integration: RESTful APIs, GraphQL, or event schemas; designing contracts and versioning strategies.
  • Performance tuning and cost optimization techniques for cloud data platforms.

Soft Skills

  • Strong written and verbal communication — translate technical trade-offs into business impact for non-technical stakeholders.
  • Cross-functional collaboration — work closely with product, analytics, compliance, and platform teams to deliver outcomes.
  • Analytical problem solving — diagnose pipeline failures, performance regressions, and data anomalies quickly and methodically.
  • Prioritization and product sense — balance technical debt, scalability, and immediate business needs while delivering incremental value.
  • Mentorship and leadership — provide constructive reviews, coaching, and knowledge sharing across the team.
  • Autonomous execution — comfortable driving projects end-to-end with minimal supervision.
  • Customer-focused mindset — collect feedback from analysts and data consumers and iterate on data products accordingly.
  • Adaptability — learn new cloud services and tooling quickly in a fast-evolving data landscape.
  • Attention to detail — produce reliable, well-tested productions artifacts and comprehensive documentation.
  • Time management and stakeholder diplomacy — manage multiple requests, set expectations, and negotiate delivery timelines.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Statistics, or equivalent practical experience.

Preferred Education:

  • Master's degree in Data Science, Computer Science, or related technical field; MBA or advanced degree in analytics or engineering leadership is a plus.

Relevant Fields of Study:

  • Computer Science / Software Engineering
  • Data Science / Machine Learning
  • Information Systems / Business Analytics
  • Applied Mathematics / Statistics

Experience Requirements

Typical Experience Range:

  • 3–7 years of hands-on experience in data engineering, analytics engineering, or related roles implementing production data systems.

Preferred:

  • 5+ years of experience building cloud data platforms, end-to-end ETL/ELT pipelines, and production-grade analytics solutions; demonstrated experience with at least one cloud provider (AWS/GCP/Azure), a modern data warehouse (Snowflake/BigQuery/Redshift), orchestration (Airflow/dbt), and streaming technologies (Kafka/Kinesis).
  • Proven track record of delivering cross-functional data products, implementing governance and security controls, and driving cost/performance optimizations in production environments.
  • Experience mentoring engineers, defining engineering standards, and contributing to organizational data strategy and roadmaps.
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