Data Team Lead

ABOUT THE COMPANY

With over 10 years of relentless innovation, MyMind Joint Stock Company (MSM) is redefining the future of enterprise management. We deliver smart, scalable digital solutions that power business transformation across industries—from enterprises and agriculture to smart manufacturing. By combining deep domain expertise with cutting-edge technology, MSM helps organizations worldwide simplify complexity, accelerate growth, and lead with confidence in the digital era.

ABOUT THE POSITION

The Data Leader owns the data function at MSM end-to-end: the data platform (lake, warehouse, DaaS), the data team, and the alignment between data and the business domains we serve — Ecommerce, ERP/DMS, and AgriTech. The role is hands-on at the architecture level while being accountable for people, delivery, and standards.

RESPONSIBILITIES

1.Team leadership & mentoring

• Lead, coach, and mentor the data engineering team (Senior/Middle/Junior); run 1:1s, define growth paths, and conduct performance reviews.

• Set technical standards through design reviews, code reviews, and pairing - raise the team's bar rather than doing all the hard work alone.

• Build the hiring pipeline: define role profiles, interview, and onboard data engineers and analysts.

2. Resource coordination & delivery

• Plan, allocate, and rebalance team capacity across squads/projects (DigiRetail, DigiFarm, DigiFactory, DigiO) based on priority, risk, and deadlines.

• Own the data roadmap and backlog - break business requests into epics/stories, estimate effort, track delivery, and report status to management and stakeholders.

• Manage cross-team dependencies with product, backend, DevOps, and data science; unblock the team and escalate early.

3. Business & domain alignment

• Understand the business data models of Ecommerce (orders, catalog, inventory, promotions, customer/CRM/CDP), ERP/DMS (sales-distribution, finance, procurement, warehouse), and AgriTech (field, crop, GIS, IoT/sensor).

• Translate business KPIs into data products: define metrics, dimensional models, and data contracts with product owners and domain experts.

• Act as the primary data counterpart for stakeholders (Product, Finance, Operations, Customers/Partners) - and own data governance, data quality, and access policies.

4. Platform & architecture

• Architect and evolve the data platform: batch/streaming pipelines, data lake/lakehouse, warehouse, and DaaS/API layers for analytics, applications, and partners.

• Develop and maintain GIS data systems enabling geospatial analytics and field-level monitoring.

• Optimize storage and compute cost/performance across on-premise and cloud; enforce infrastructure-as-code, versioning, and reproducibility.

• Drive AI/ML enablement on the platform: feature pipelines, model serving integration, and AIready data architecture (Nice to Have).

REQUIREMENTS:

• Experience: 7+ years in data engineering / data platform, including 2+ years leading a data team (3+ members) with direct people-management responsibility.

• Leadership & mentoring: proven track record of growing engineers, running design/code reviews, and setting team standards.

• Resource & delivery management: capacity planning, prioritization, estimation, and status reporting across multiple concurrent projects; hands-on with Jira/agile delivery.

• Business domain knowledge: solid understanding of Ecommerce and/or ERP/DMS data (transactional models, master data, KPIs, reconciliation, reporting needs); experience working directly with business stake

Core technical skills:

• Proficient in Python and SQL for data transformation and automation.

• Strong experience with distributed processing frameworks (e.g., Apache Spark, Flink). o Experience designing and operating data lakes/lakehouses and warehouses (e.g., Iceberg, Delta Lake, BigQuery, Redshift).

• Experience designing APIs or DaaS platforms for data delivery.

• Solid knowledge of data modeling (Kimball, Data Vault, or similar) and data governance/quality frameworks.

• Familiarity with semi-structured and unstructured data (JSON, geospatial, IoT, sensor data).

Tools/Software proficiency:

• Orchestration tools (Airflow, Dagster, Prefect).

• Cloud platforms (AWS preferred; GCP or Azure).

• Git, CI/CD, and infrastructure-as-code (Terraform or equivalent).

Nice to Have Skills

• AI Engineer experience (strong advantage): designing AI/ML architecture on AWS — e.g., SageMaker, Bedrock, Lambda/Step Functions, feature store, vector databases, model serving/MLOps — and integrating AI into data pipelines (data enrichment, anomaly detection, predictive modeling for AgriTech/Ecommerce use cases).

• Education: Bachelor's or Master's in Computer Science, Data Engineering, or related fields.

• Mindset: practical, detail-oriented, collaborative, and curious about how data can improve agriculture and retail operations.

Advanced tools/technology skills:

• Geospatial tools and libraries (PostGIS, GeoPandas, QGIS).

• Data catalog or governance tools (OpenMetadata, DataHub).

• Streaming platforms (Kafka, Pulsar).

• Semantic/BI layer and self-service analytics (dbt, Metabase, Power BI, or similar).

• Certifications: AWS Certified Data Engineer / Solutions Architect / Machine Learning Specialty.